Methods of monitoring and analyzing metabolic activity profiles diagnostic and therapeutic uses of same

ABSTRACT

A method of measuring a metabolic activity (MA) of a cell is provided. The method comprising independently measuring in an extracellular environment of the cell, time-dependent acidification profiles due to secretion of: 
     (i) non-volatile soluble metabolic products;
 
(ii) non-volatile soluble metabolic products and volatile soluble metabolic products; and
 
(iii) volatile soluble metabolic products;
 
wherein at least one of the time dependent acidification profiles is indicative of the metabolic activity of the cell. Also provided are clinical methods which make use of the assay.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/817,543 filed on Feb. 19, 2013, which is a National Phase of PCTPatent Application No. PCT/IL2012/050125 having International filingdate of Apr. 4, 2012, which claims the benefit of priority under 35 USC§119(e) of U.S. Provisional Patent Application No. 61/472,213 filed onApr. 6, 2011. The contents of the above applications are allincorporated by reference as if fully set forth herein in theirentirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to methodsof monitoring and analyzing metabolic activity profiles and diagnosticor therapeutic uses of same, or specifically relates to cancer diagnosisby metabolic activity monitoring of blood samples.

A major problem in disease treatment remains early detection andstaging. Early detection enables therapeutic treatment from the onset ofthe disease resulting in successful treatment in many cases. Staging ofa disease might indicate on the appropriate protocol of medication whichmight be decisive for optimal treatment. For example today, millions ofpeople are living with cancer or have had cancer. Cancer is the secondmost common cause of death in the United States, exceeded only by heartdisease. Cancer accounts for nearly 1 out of every 4 deaths in theUnited States. The sooner a cancer is diagnosed and treated, the betterthe survival chances are.

All known methods for detection of cancer focus on identifying mostlythe malignant tissue and/or its pathological cancer biomarkers secretedto the circulation. However, these diagnostic methods are onlyunfortunately effective at relatively advanced stages of the disease.

The Warburg effect is the observation that most cancer cellspredominantly produce energy by a high rate of glycolysis followed bylactic acid production n in the cytosol, rather than by a comparativelylow rate of glycolysis followed by oxidation of pyruvate in mitochondrialike most normal cells [Kim J W, Dang C V (2006). “Cancer's molecularsweet tooth and the Warburg effect”. Cancer Res. 66 (18): 8927-30].Second, in 1920s Otto Warburg found that cancer cells^(19,20), incontrast to normal differentiated cells, primarily rely on aerobicglycolysis rather than on mitochondrial oxidative phosphorylation togenerate ATP as the fuel for energy needed for cellular processes. Thishistorical phenomenon was termed “the Warburg effect”²¹. Otto Warburgpostulated that this change in metabolism is the fundamental cause ofcancer [Warburg O (1956). “On the origin of cancer cells”. Science 123(3191): 309-14], a claim now known as the Warburg hypothesis. About 50years later the Warburg effect was also observed in activated lympocytesin vitro see e.g., Maclver et al. 2008 J. Leukocyte Biology 84:1-9; andDeBerardinis Cell Metabolism 7:11-20. The Warburg effect was found alsoin the immune system where activated T cells^(22,23) rapidly hyperinduceglycolysis, for example by over-expression of glucose transporters(GLUT)²⁴.

The Warburg effect has important medical applications, as high aerobicglycolysis by malignant tumors is utilized clinically to diagnose andmonitor treatment responses of cancers by imaging uptake of2-¹⁸F-2-deoxyglucose (FDG) (a radioactive modified hexokinase substrate)with positron emission tomography (PET). See also WO2007/102146.However, these methods are cumbersome and expensive by requiringhigh-tech facilities or in-situ tissue biopsies.

Therefore, non-invasive methods for early and simple diagnosis areneeded.

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present inventionthere is provided a method of measuring a metabolic activity (MA) of acell, the method comprising independently measuring in an extracellularenvironment of the cell, time-dependent acidification profiles due tosecretion of:

(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products;(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity of the cell.

According to an aspect of some embodiments of the present inventionthere is provided a method of diagnosing a disease associated with amodified metabolic activity in a subject-in-need thereof, the methodcomprising:

(a) providing a biological sample of the subject which comprises a cell;(b) independently measuring in an extracellular environment of the celltime-dependent acidification profiles due to secretion of:(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products; and(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity of the cell and whereas a shift inthe metabolic activity compared to that of a normal unaffected cellsample examined under identical conditions is indicative of a diseaseassociated with modified metabolic activity.

According to an aspect of some embodiments of the present inventionthere is provided a method of individually optimizing disease treatment,the method comprising:

(a) contacting a biological sample of the subject which comprises a cellwith at least one medicament;(b) independently measuring in an extracellular environment of the celltime-dependent acidification profiles due to secretion of:(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products; and(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity of the cell and whereas a shift inthe metabolic activity of the cells towards that of a normal healthycell sample examined under identical conditions is indicative of anefficacious medicament for the disease.

According to an aspect of some embodiments of the present inventionthere is provided a method of monitoring disease treatment in a subject,the method comprising:

(a) administering at least one medicament against the disease to thesubject;(b) retrieving a biological sample which comprises a cell of the subjectfollowing the administering;(c) independently measuring in an extracellular environment of the celltime-dependent acidification profiles due to secretion of:(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products; and(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity of the cell and whereas a shift inthe metabolic activity of the cells towards that of a normal healthycell sample examined under identical conditions is indicative of anefficacious treatment of the disease.

According to an aspect of some embodiments of the present inventionthere is provided a method of disease treatment in a subject in needthereof, the method comprising:

(a) diagnosing a presence of the disease in the subject according to themethod of claim 2;

(b) treating the subject based on the diagnosis.

According to an aspect of some embodiments of the present inventionthere is provided a method of identifying an agent capable of altering ametabolic activity of cells, the method comprising:

(a) subjecting cells to an agent;

(b) measuring the metabolic activity of the cells following (a) andoptionally prior to (a) according to the method of claim 1, wherein ashift in the acidification profiles is indicative of an agent capable ofaltering a metabolic activity of cells.

According to some embodiments of the invention, the extracellularenvironment comprises a defined solution having a calibrated buffercapacity.

According to some embodiments of the invention, the buffer comprises aphosphate buffered saline.

According to some embodiments of the invention, the cell comprises aleukocyte.

According to some embodiments of the invention, the cells comprise acancer cell.

According to some embodiments of the invention, the cells comprise aperipheral blood mononuclear cell (PBMC).

According to some embodiments of the invention, the disease comprisescancer.

According to some embodiments of the invention, the biological samplecomprises a blood sample.

According to some embodiments of the invention, the disease is selectedfrom the group consisting of cancer, pathogenic infection and anautoimmune disease.

According to some embodiments of the invention, the measuring iseffected using a non-toxic membrane impermeable probe selected from thegroup consisting of a pH probe, a CO₂ probe and NH₃ probe and a lactateprobe.

According to some embodiments of the invention, the pH probe comprises aratiometric pH probe.

According to some embodiments of the invention, the pH probe comprisesHPTS.

According to some embodiments of the invention, the non-volatilemetabolites comprise lactate.

According to some embodiments of the invention, the volatile metabolitescomprise NH₃ and CO₂.

According to some embodiments of the invention, the measuringacidification profile of (i) is effected in air-exposed chambers.

According to some embodiments of the invention, the measuringacidification profile of (ii) is effected in air-sealed chambers.

According to some embodiments of the invention, the measuringacidification profiles is effected at a constant temperature.

According to some embodiments of the invention, the constant temperaturecomprises 37° C.

According to some embodiments of the invention, the method furthercomprises subjecting the cell to a stimulant or inhibitor prior to, orconcomitant with measuring the acidification profile.

According to some embodiments of the invention, the stimulant orinhibitor comprises a cell.

According to some embodiments of the invention, the stimulant orinhibitor comprises a cell-free antigen.

According to some embodiments of the invention, the stimulating cellcomprises a lymphocytes and the cell comprises a non-syngeneiclymphocyte with respect to the lymphocyte.

According to some embodiments of the invention, the measuringacidification profiles is effected in commercial fluorescence multi wellplate scanner.

According to some embodiments of the invention, signal to noisefiltering of the MA test background measures is carried out by k-meanscluster analysis.

According to some embodiments of the invention, the diagnostic decisionsby the metabolic activity measures are subject to at least two decisiontree models.

According to some embodiments of the invention, the decision tree modelsare selected from the group of C5, C&R Tree and CHAID.

According to some embodiments of the invention, the method furthercomprises separating the cell from the extracellular environment.

According to some embodiments of the invention, the separating is byficoll separation under centrifugation.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a schematic representation of the differences betweenoxidative phosphorylation, anaerobic glycolysis, and aerobic glycolysis(also known as the “Warburg effect”).

FIG. 2 is a graph showing pH-dependent absorption spectra of8-hydroxypyrene-1,3,6-trisulfonic acid (HPTS).

FIGS. 3A-D are graphs showing pH and acidity calibration of workingsolution at 2 mM and 10 mM phosphate buffers saline, at 1 μM HPTS. OPEN;The acidification steps were monitored at 37° C. without seal. CLOSE;The acidification steps were monitored at 37° C. after the multi-wellplate was sealed.

X axis; The Ratio: (Fluorescence Intensity at Ex. 403 nm)/(FluorescenceIntensity at Ex. 455 nm.)

Right Y axis (triangles): The accumulated quantity of HCl (μmol/ml) asobtained by the sequential addition of 1N HCl.

Left Y axis (circles): The appropriate pH values as measured by pH glasselectrode. FIGS. 3A-B—Working solution with 2 mM phosphate buffersaline. FIGS. 3C-D—Working solution with 10 mM phosphate buffer saline.

FIGS. 4A-B are graphs showing calibration curves at HPTS concentrationsof 1 μM and 10 μM at 10 mM phosphate buffer saline.

X axis: The Ratio: (Fluorescence Intensity at Ex. 403 nm)/(FluorescenceIntensity at Ex. 455 nm.)

Right Y axis (triangles): The accumulated quantity of HCl (μmol/ml) asobtained by the sequential addition of 1N HCl.

Left Y axis (circles): The appropriate pH values as measured by pH glasselectrode. (FIG. 4A) Final concentration of HPTS is 1 μM. (FIG. 4B)Final concentration of HPTS is 10 μM.

FIGS. 5A-D show k-means cluster analysis of the HPTS reference rateValues. FIGS. 5A-C—x-axis point on probe's standardized values receivedfrom “OPEN”, and y-axis point on probe's standardized values receivedfrom “CLOSE” measurements. FIG. 5A—Examination of all values from alltests donors (N=730 observations) before cluster analysis. FIG.5B—k-means cluster analysis of probe data indicates on 26 clusterspresented with different colors. Five clusters were found small(observations ≦6) and therefore were discarded (outliers). FIGS. 5C-D—34observations (4.66%) from 730 observations were excluded (red). Theremaining 696 observations (95.34%) (blue) are presented in 21 separateclusters in (FIG. 5B). Then the mean values of “OPEN” and mean values of“CLOSE” states were recalculated for each donor.

FIGS. 6A-C are MA profiles for increasing glucose concentration obtainedfor typical healthy and cancer donors x axis: Glucose concentration(mM). y axis: Metabolic activity rate of hPBMCs in units ofpicomolH+/μl/hour/2500 PBMCs for FIGS. 6B-C and picomolH+/μl/hour forFIG. 6C. The acidification kinetics is measured during 1 hour ofincubation at 37° C. “OPEN” state cycle of the multi well plate during30 minutes. In this state there is gas ventilation of CO₂ and NH₃, sothat only lactate acid production (including other non-volatile organicacids) contributes to the equivalent acidic accumulation in each well.“CLOSE” state cycle of the same multi well plate during 30 minutes. Inthis hermetically sealed state, CO₂ and NH₃ react at equilibrium withwater to form carbonic acid and ammonium ions. The acidity level isproduced by both the lactic and carbonic acid anions around pH 7.3. TheNH₄ ⁺ basic cation is evaluated here to titrate the acidity level.“CLOSE”-“OPEN”=CO₂+(—NH₃)). (FIG. 6A) a control record of the MA testincluding the probe HPTS and glucose, but without cells. (FIG. 6B) MAprofiles of a 45 years old female representing a typical healthy donor(similar profiles are obtained for different age and gender). (FIG. 6C)MA profiles of a 37 years old female with Breast idc cancer at stage 2and before treatment. Note induced MA changes between healthy anddiseased samples may be detected already in the basal state (controlsample without stimulants).

FIGS. 7A-D are graphs showing a case study follow-up of MA profiles forincreasing glucose obtained for a 65 years old female with breast cancercompare to a 69 years old healthy male. Black—“CLOSE”; Red—“OPEN”;Blue—“C-O”=“CLOSE”-“OPEN”. FIGS. 7A—MA profiles of peripheral bloodmononuclear cells (PBMCs) of a 69 years old healthy male. FIGS. 7B-Dshow 3 follow-up MA profiles of peripheral blood mononuclear cells(PBMCs) of a 65 years old female with breast idc cancer. X axis: Glucoseconcentration (mM). Y axis: Metabolic activity rate of PBMCs in units ofpicomol/μl/hour/2500 PBMCs. (FIG. 7B) The first MA test of the follow-uppatient, already suspicious by the test results to have cancer (time=0).(FIG. 7C) time=+10.5 months—The second MA test just after routinemammography diagnosed by the physicians to have breast idc Cancer instage 3. (FIG. 7D) time=+14.5 months—The sixth MA test was carried outafter surgical removal of tumor of 2.2×2.4 cm in left breast and after 2months of 3 chemotherapy treatments.

FIGS. 8A-D are graphs showing MA profiles for increasing PSAconcentration obtained for typical healthy donors, breast cancer patientand breast cancer recovered donor.

FIG. 8A—a control record of the MA test including the probe HPTS andPSA, but without cells (FIGS. 8B-D) MA profiles of peripheral bloodmononuclear cells (PBMCs) of 3 different donors. X axis: PSAconcentration (μg/ml). Y axis: metabolic activity rate of PBMCs in unitsof picomole H+/μl/hour/2500 PBMCs for FIGS. 9B-D and picomole H+/μl/hourfor FIG. 8A. The acidification kinetics is measured during 1 hour ofincubation at 37° C. Black—“CLOSE”; Red—“OPEN”;Blue—“C-O”=“CLOSE”-“OPEN”. (FIG. 8A) MA profiles of a 59 years oldfemale, representing a typical healthy donor. (FIG. 8B) MA profiles of a37 years old female with breast idc cancer in stage 2 and before anytreatment. (FIG. 8C) MA profiles of a 50 years old female recovered frombreast cancer 18 years prior to the MA test.

FIGS. 9A-D show model building and classification evaluation for the MAtest results.

FIGS. 9A-B—First group of donors in the age above 40 (n=42). FIGS.9C-D—Second group of donors in the age between 22 to 81 years old. FIGS.9A, C—The two tables present the best models with the best cutting pointof the best classification. “O” refers to “OPEN” state, “C” refers to“CLOSE” state and “C-O” refers to “CLOSE-OPEN” state. TP refers to truepositive, FN refers to false negative, TN refers to true negative and FPrefers to false positive FIGS. 9 B,D—two graphs for the evaluation andcomparison of the models performance by the cumulative gain charts. They axis shows the percentage of donors classified by the models to havecancer. This is a percentage of the total donors (healthy and cancerpatients). The x axis shows the percentage of patients classified tohave cancer, which is a fraction of the 42 total donors for the firstgroup and 67 total donors for the second group. Presented are the 4 bestmodels that were able to classify with high accuracy both healthy donorsand cancer patients. The fourth model is a logistic regression modelfrom the family of regression models. Black line refer to randomresponse rate (if randomly classified X % of donors, X % of cancerpatients are obtained). Sky blue line refers to the theoretical bestmodel. Red line refers to CHAID model, Green line refers to C5algorithm, Yellow line refers to C&R tree model and Blue line refer tologistic model.

FIGS. 10A-D present evaluating model results of the MA test using avalidation set of 30% of the donors. FIGS. 10A-B—First group of donorsin the age above 40 (n=42). FIGS. 10C, D—Second group of donors in theage from 22 to 81 years old. (a, c) Data as described in FIG. 9 wasrandomly partitioned into two groups of “Training” and “Testing” usingthe clementine software V13.0. FIG. 10A—Validation set included 70% ofthe donors for the first group. FIG. 10C—Validation set included 70% ofthe donors for the second group. FIGS. 10A, C—Two graphs for theevaluation and comparison of the models performance made by thecumulative gains charts after random data partition. The y axis showsthe percentage of donors classified by the models to have cancer. Thisis a percentage of the total donors (healthy & cancer patients). The xaxis shows the percentage of patients classified to have cancer. The“Training” set are used to build the data mining model on 70% of thedonors. The remaining 30% of the donors are later used to evaluate theclassification result on the “Testing” set using the models that weregenerated in the training set (CHAID, Logistic, C5, C&R tree). Asdescribed in FIG. 9, sky blue line refers to the theoretical best model,Red line to CHAID model, Green line to C5 algorithm, Yellow line to C&Rtree model, Blue line to logistic model and black line refers to randommodel. FIGS. 10B, D—The two tables present the best model with the bestcutting point of the best classification after random data partition.“O” refers to “OPEN” state, “C” refers to “CLOSE” state and “C-O” refersto “CLOSE-OPEN” state. TP refers to true positive, FN refers to falsenegative, TN refers to true negative and FP refers to false positive. Inboth groups of donors C5 was the best performer in the “Testing” setwhile in the “Training” set C&R tree was the best performer.

FIG. 11 depicts a working hypothesis: metabolic activity profiles ofhPBMCs as a mirror image of tumor development. Cancer development isconsidered to be associated with changes in the physiological functionof the immune system that might be reflected in different metabolicactivity (MA) profiles of the hPBMCs. The Y axis presents the two armsof metabolic pathways', namely—oxidative phosphorylation versus aerobicglycolysis. The X axis presents the stages of tumor development fromhealthy to metastasized cancer. Quiescent cells (Q) have a dominantbasal rate of oxidative phosphorylation. Under initial stages of tumordevelopment there is “clonal expansion” of relevant tissue specific subgroup of Q (Qi) into antitumor effector killer cells (Eki), which atlater stages are probably transformed into protumor effector feedercells (Efi). Concomitantly, immune tolerance and anergy succumb into themetastasized stage, where both tissue specific effector subgroups may beexhausted, including their respective quiescent subgroup (Qi). Thepresent results reveal a metabolic shift from dominant oxidativephosphorylation preferred by hPBMCs of healthy donors to dominantaerobic glycolysis (“Warburg Effect”) preferred by hPBMCs of variouscancer patients at the stages of local tumor development (stage 1-3).The shift towards aerobic glycolysis may be related to a dominant Efisubgroup and possibly also Eki subgroup. The ability of tissue-specificearly detection of cancer (stage 0) will be examined by a follow upprotocol. It is expected to be revealed as enhanced/reduced metabolicactivation under stimulation by tissue—specific antigens, compared tothose obtained for healthy donors. In early metastasized cancer (stage4) a gradual backward shift towards dominant oxidative phosphorylationis expected, yielding a diagnostic tool for the appropriate treatment.The above schematic description of characteristic metabolic activityprofiles is presented by the striped orange line which is related to thedifference “close-open” results of the MA test, which indicate theWarburg effect shift of hPBMCs.

FIG. 12 is a flowchart of the MA test protocol and analysis framework.The sequential phases A-E of the MA test framework fit those detailed inExample 1 of the Examples section which follows.

FIGS. 13A-I MA Profiles for Increasing Glucose Concentration Obtainedfor typical healthy, cancer, and autoimmune lupus donors. X axis:Glucose concentration (mM). Y axis: Metabolic activity rate of PBMC inunits of picomole H⁺/ul/hour/2500 cells. The acidification kinetics ismeasured during 1 hour of incubation at 37° C.

FIGS. 13A, D, G—“CLOSE”; 13B, E, H—“OPEN”; 13C, F,I—“C-O”=“CLOSE”-“OPEN”. FIGS. 13A-C—Representative MA profiles of atypical healthy donor. FIGS. 13D-F—Representative MA profiles of atypical cancer patient. FIGS. 13G-I—MA profiles of a patient withsystemic lupus (an autoimmune disease).

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to methodsof monitoring and analyzing metabolic activity profiles and diagnosticor therapeutic uses of same, or specifically relates to cancer diagnosisby metabolic activity monitoring of blood samples.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details set forth in the following description orexemplified by the Examples. The invention is capable of otherembodiments or of being practiced or carried out in various ways.

Today, high-throughput methods are urgently required for early detectionand staging of various diseases. For example, the sooner a cancer isfound and treated, the better the survival chances are. Furthermoreidentifying the disease's stage ensures the appropriate treatment.

The present inventors have realized that in contrast to standardapproaches for diseases detection that analyze various in situparameters or relevant circulatory markers associated with the diseasethat allow disease detection at relatively late stages, the immunesystem might mirror disease state already at onset of the disease. Sincethe immune system is naturally responsible to combat disease developmentalready at early stages it would be beneficial to identifycharacteristic profiles of relevant immune responses. Variation of suchprofiles of the metabolic activity of the immune system along withdisease development may become useful also for disease staging. Forexample, in homeostasis, the immune system activity should be wellcontrolled; hyperactivity is associated with autoimmune diseases whilecancer development is probably related to hypoactivity of the immunesystem. These opposite routs might be indicated by general and morespecific MA profiles in response to various nutrients and stimulants.

The present inventors have thus devised a ground-breaking,clinically-oriented approach for quantitative measurement of themetabolic activity of relevant cell populations, as an indicator ofdisease. The assay measures the rate of the metabolic activity ofmicroliter cellular samples, by monitoring extracellular acidificationusing a pH-sensitive impermeable fluorescence probe.

As is illustrated in the Examples section which follows, using themetabolic activity (MA) analysis, the present inventors have revealed asignificant shift between different metabolic pathways monitored onPBMCs obtained from cancer patients and healthy donors. This shift maybe adopted as a diagnostic tool, for a clear-cut differentiation betweenhealthy and cancer patients by monitoring characteristic changes in themetabolic activity of PBMCs (FIGS. 6-10, 13). These significantpreliminary findings were obtained by comparing the MA test results in“open” versus “close” (air-sealed) wells. Both records enable to measurethe accumulations of soluble versus volatile metabolic products (lacticacid versus CO₂ and NH₃), thereby differentiating between threemetabolic pathways—oxidative phosphorylation, anaerobic glycolysis andaerobic glycolysis, as interpreted below.

Non activated T cells (naive T cells), like most normal differentiatedcells, rely primarily on mitochondrial oxidative phosphorylation toefficiently generate ATP for the energy needed for cellular processes,and the volatile CO₂ product. In the absence of oxygen they must rely onmuch less efficient metabolic pathway of ATP production, associated withlactic acid production known as anaerobic glycolysis. In contrast, mostcancer cells¹⁸ are found to rely on aerobic glycolysis, which is similarto the anaerobic glycolysis despite the presence of oxygen. Thisphenomenon was originally found by Otto Warburg in relation to cancercells, and termed “the Warburg effect”. The present inventors revealedthe presence of the “Warburg effect” in fresh PBMCs of cancer patients.Without being bound by theory, the immunometabolic rational of the“Warburg effect”, namely the shift between naive and activatedlymphocytes in fresh PBMCs of cancer patients, may be related to theneed of aggressive and effective physiological function of activated Tcells in the tumor cells neighborhood, where at early stages beforeangiogenesis it is probably oxygen deficient. This idea is consistentwith the fact that tumor cells are initially adapted to oxygendeficiency through the “Warburg effect”.

In light of the above metabolic pathways, the end products, CO₂ andlactate contribute directly to the acidification examined by the MAtest.

Moreover, another end product which is considered to play a major rolein the MA test is Ammonia (NH₃). One of the primary sources of cellenergy is protein catabolism, which is the process of protein brake downto amino acids. Amino groups are removed from amino acids and convertedto ammonia. Another source of cellular NH₃ production is throughmetabolic pathways of purines and pyrimidines making up the two groupsof nitrogenous bases. In the present measurement system, as in vivo,vital cells must maintain the cytoplasm in a constant pH of about7.2-7.4 by secretion of the metabolic acidic and basic products, such aslactic acid, carbonic acids and the ammonium base.

These findings already assure that by communicating with the immunesystem the physiologically-oriented MA analysis could have significantimplications for developing new ways to detect, diagnose and treatcancer, as well as other diseases.

Thus, according to an aspect of the invention, there is provided amethod of measuring a metabolic activity (MA) of a cell. The methodcomprising independently (i.e., separately) measuring in anextracellular environment of the cell, time-dependent acidificationprofiles due to secretion of:

(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products;(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity pathway of the cells.

As used herein “metabolic activity pathway” refers to the relativecontribution of mitochondrial oxidative phosphorylation, anaerobicglycolysis, aerobic glycolysis and NH₃ ⁺ production to energyproduction.

The profiles may have a spike configuration or a monotonic saturatedbehavior.

A spikes profile typically reflects receptor mediated stimulation ofmetabolic activity which is expected to be more specific compared to theconcentration dependent nutrient response. The latter response isgenerally a monotonic saturated profile.

As used herein “cell” refers to a prokaryotic or a eukaryotic cell forwhich the above metabolic activity can be measured. The cell can be abacteria, yeast, plant, insect or mammalian cell. According to aspecific embodiment, the cell is a human cell. It will be appreciatedthat the cell may refer to a single cell but may also refer to aplurality of cells. The cells may be isolated cells (having no tissueorganization) or cells in a tissue or tissue fragment. According to aspecific embodiment, when the cells are PBMCs, the assay is done on10³-10¹⁰ cells. According to a specific embodiment the number of cellsis 10⁶-10⁷.

The cell may be a differentiated cell, a non-differentiated cell (e.g.,stem cell) or a dedifferentiated cell.

According to a specific embodiment, the cell is a cell of the immunesystem, that is a white blood cell (i.e., a leukocyte). Examplesinclude, a neutrophil, an eosinophil, a basophil, a lymphocyte (T cellor B cell), a monocyte, a macrophage and a dendritic cell.

According to another embodiment, the cell is a pathogenic or diseasedcell of any tissue such as a cancer cell. Other diseases and medicalconditions which can be detected according to the present teachings areprovided below.

Other cells which may be analyzed according to the present teachingsinclude, but are not limited to, en embryonic cell (such as for WFqualification), a red blood cell, a platelet, a bacterial-infected cell,a fungus-infected cell, and a viral infected cell.

Thus, the cell may refer to an isolated population of cells whichcomprise a highly purified subset of specific cells i.e., homogenic cellpopulation (e.g., >80% purity), e.g., T cells, or a heterogenic cellpopulation which comprises various types of immune cells such asperipheral blood leukocytes (PBL) or mononuclear cells.

Cells may be non-cultured, cultured primary cells or cloned cells (e.g.,cell-line).

The cells may be adherent cells or cells in suspension.

According to further embodiments, the cells can be non-geneticallymodified or genetically modified.

As used herein “independently measuring” refers to separate measuring ofitems (i), (ii) and possibly (iii). Although it will be appreciated,according to a specific embodiment, that (iii) is the result ofsubtracting (i) from (ii). These separate measurements can be performedin parallel, simultaneously, on identical yet separate cell samples, orsequentially on a single cell sample (as described in the Examplessection which follows).

Thus, measuring extracellular acidification profile is performed by thecalibrated curve of acidification (Table 1).

Measurement of metabolic activity is performed by calculating theaccumulated acidification in relation to the fluorescencently measuredpH changes in the extracellular environment of the cells (e.g.,pmol/ul/hour/2500 cells) in “open” and “close” state. It will beappreciated that, according to a specific embodiment, this measurementis performed only in the extracellular environment of the cell and notintracellularly. Extracellular pH measurement is advantageous since inthe extracellular environment there is a persistent acidic accumulationversus a relatively small average changes in the transient intracellularresponses due to homeostatic physiological regulation; there is nophysiological interference of the extracellular probe with intracellularprocesses; there is a comparative high signal to noise ratio of theextracellular ratiometric fluorescent probe; simplicity of fluorescentmedium (calibrated buffer capacity) preparation versus cellularmanipulations; there is no background fluorescence in contrast tosignificant leakage of intracellular probes; kinetic measurements aremade with no need for permeabilization procedures, thereby allowing theanalysis of live cells in real-time; there are minimal problemsassociated with quenching and oxidation effects; and finallysimultaneous high throughput kinetic measurements are enabled withoutthe above hurdles.

As used herein “an extracellular environment” of the cell refers to anatural environment e.g., blood or plasma, or an artificial environmentsuch as a culture medium

According to a specific embodiment, the MA test is effected in a definedsolution (all components are known) having a calibrated bufferedcapacity.

It will be appreciated that the buffer capacity should ensure minorchanges in the physiological pH.

According to a specific embodiment, the buffer is a phosphate buffer(e.g., phosphate buffer saline 1-10 mM or 10 mM phosphate buffer). Itwill be appreciated that low buffer concentration is required foracidification measurements at low cell concentration. According to aspecific embodiment 10 mM phosphate buffer saline is used for 2.5×10⁶cells/ml.

Thus, kinetics of metabolic activity is monitored during the incubationby a minor acidification process of a HPTS fluorescence calibratedbuffer capacity.

FIGS. 3A-D and 4A-D describe the working solution calibration and probecalibration, respectively.

According to a specific embodiment, measuring the acidification profilesis performed at a constant temperature, e.g., 20-40° C. or specifically,at optimal growth temperature, say 37° C. for mammalian cells.

As described hereinabove, the extracellular acidification profiles areindicative of the identity of the various metabolic products secreted bythe cell.

As shown in FIG. 1, a tumor or proliferative tissue (e.g., activated Tcells) use preferentially aerobic glycolysis which is characterizedmainly by the secretion of Lactate to the medium. In contrast, adifferentiated tissue will employ oxidative phosphorylation or anaerobicglycolysis and therefore will secrete CO₂ or lactate, dependent on theavailability of oxygen, respectively.

According to a specific embodiment, time dependent acidification profiledue to secretion of non-volatile soluble metabolic products mainlylactate is performed in an air-exposed chamber. Under such conditions(“open”), there is gas ventilation of CO₂ and NH₃, so that only lactateacid production (including other non-volatile organic acids) contributesto the equivalent acidic accumulation in each well.

According to a specific embodiment, time dependent acidification profiledue to secretion of non-volatile soluble metabolic products and volatilesoluble metabolic products is effected in an air-sealed chamber. In thehermetically sealed state (“close”), CO₂ and NH₃ react at equilibriumwith water to form carbonic acid and basic ammonium ions. In this state,however, the NH₄ ⁺ basic cation titrates the acidity level produced byboth the lactic and carbonic acid anions around pH 7.

According to a specific embodiment, the acidification kinetics ismeasured in 30 minutes sequence of air “open” and “closed” states of themulti well plate.

By the appropriate rates (V), of acidification (+) and basic titration(−), the total measured rates of acidification in the open state (Vopen)and the closed state (Vclosed) are described by the coupled equations:

Vopen=V(lactic acid).

Vclose=V(lactic acid)+V(carbonic acid)−V(ammonium base).

Using this configuration, the time-dependent acidification profile dueto secretion of volatile soluble metabolic products is calculated by thesubtraction of the profiles of (ii)-(i).

Measuring the kinetics of extracellular acidification is performed usinga non-toxic membrane impermeable probe. Examples include, but are notlimited to, a ratiometric pH probe, a CO₂ probe, an NH₃ probe, a lactateprobe and a combination of same. According to a specific embodiment theratiometric technique is required for the high sensitivity at pHbuffered conditions.

Examples of specific probes which can be used according to the presentteachings include, but are not limited to, HPTS, CFDA and carboxyfluorescein. Such probes are commercially available such as fromMolecular Probes.

According to a specific embodiment, measuring the acidification iseffected using the ratiometric pH probe8-Hydroxypyrene-1,3,6-trisulfonic acid (HPTS).

HPTS is a cost effective, non-toxic, highly water-solublemembrane-impermeant pH indicator with a pKa of ˜7.3 in aqueous buffers.HPTS exhibits a pH-dependent absorption shift, allowing ratiometric pHmeasurements as function of the ratio between the fluorescenceintensities at 513 nm that are measured sequentially under excitation at455 nm and 403 nm. This method is essential for the present sensitivemeasurements of minor pH changes in the physiological range around pH 7.

According to a specific embodiment. the fluorescent probe is attached toa nanoparticle, as nanosensors, in order to expand the ratiometricspecific optical monitoring of various metabolic products: CO₂, NH₃,lactic acid etc. Intracellular fluorescence measurements are extremelyuseful in basic research of the physiological mechanisms of stimulation,e.g. for calcium mobilization and membrane depolarization. However,under the homeostatic cellular response, these intracellular stimulationsignals become transient. Therefore they are considered much lesssuitable for sensitive monitoring of PBLs stimulation, compared to theongoing accumulative extracellular acidification that is recorded in theMA test. Such extracellular monitoring may be better facilitated byattachment of ratiometric molecular optical probes to nanoparticles.Extracellular monitoring is biocompatible, minimizing negative effectscommon to intracellular probes measurements, pointing on the advantageof the extracellular methods not only in basic research but also forvarious clinical applications in different cell types.

The acidification profiles are presented by the rate of secretion ofH₂O—H⁺ equivalents, in units of picomole/μl/hour/2500 cells (see FIGS.6-8, 13).

Any of the above acidification profiles can be used as an indicator ofthe metabolic activity of the cell. Alternatively, only one of themeasured profiles is indicative of the metabolic activity of the cell.

As mentioned, the metabolic activity of the cell can be measured innaïve cells or activated/effector cells which have been exposed todifferent concentrations of a stimulant or an inhibitor.

As used herein a “stimulant” or an “inhibitor” refers to an entity thatincreases, decreases or changes a metabolic pathway of a cell inresponse thereto.

For instance, if the cell is a lymphocyte then the stimulant is anantigen that is recognized by the TCR or BCR and leads to clonalexpansion or antibody production. Specific stimulants or inhibitors arelisted in Table 1 below.

TABLE 1 Units Concentration Role ug/ml 0.4-50 PHA-L is a potent mitogenfor lymphocytes H (PHA) ug/ml  0.8-100 ConA is a lectin that binds toglycoproteins C (CONA) expressed on the T cell surface, therebymimicking the T cell receptor activation which bypasses the requirementof co-stimulatory signals. ng/ml 0.03-10  Phorbol myristate acetate(PMA) mimic P (PMA) diacyglycerol and activates protein kinase C andthus eventually T cells. PMA acts selectively on a T-lymphocytesubpepulation that has high affinity for sheep erythrocytes (SRBC) andis distinct from that responsive to PHA. ng/ml 0.03-10 Lipopolysaccharide (LPS) strongly activates S (LPS) macrophages,monocytes and B cells. ug/ml 0.4-50 Myalin basic protein (MBP)-specificlymphocytes M (MBP) (CD4, CD8 cells, and NK cells (CD56+ CD3−)) (26)(87-99aa) ug/ml 0.08-1  A normal tissue antigen. This peptide(H-Ala-Ala- N(MelanA) Gly-Ile-Gly-Ile-Leu-Thr-Val-OH) is the (27-35aa)immunodominant epitope recognized by most melanoma-specific, cytoxic Tlymphocytes (CTL). ug/ml 0.4-50 A normal prostate tissue specificantigen. This A(PSA) peptide is recognized by cytotoxic T lymphocytes(146-154aa) (CTL). It is found as a biochemical marker of prostatecancer and breast cancer. mM 0.04-5  It has been known for many yearsthat lymphocytes G (glucose) and macrophages utilize glucose at a highrate, and until the important role of glutamine was identified, glucosewas considered to the only fuel used to provide energy for cells of theimmune system. mM 0.03-4  Glutamine is utilized at high rates byisolated cells L (l-glutamine) of the immune system. It is analternative energy source especially for cells that have high energydemands. nM 0.6-50 Inhibits the mammalian target of rapamycin (Mtor) R(Rapamycin) pathway by directly binding the mTOR Complex1 (Mtorc1). Arecent study report that rapamycin might be particularly effective inblocking of activated T and B lymphocytes. Another study report that itselectively promotes activation and expansion of highly suppressive Tregulatory cells.

Other Examples are provided are provided hereinbelow.

The stimulant or inhibitor may be a cell or cell-associated stimulant orinhibitor. Examples of stimulating cells include, but are not limitedto, leukocytes, stem cells, platelets, red blood cells, bacteria andfungi. Such a cellular stimulant or inhibitor may refer to an intactcell or a cell fragment e.g., cell membrane.

The use of a cell stimulant is specifically advantageous in mixedlymphocyte reactions (MLR) for use in transplantation applications suchas for prediction of graft rejection (tissue matching), preventing ortreating graft versus host disease or graft rejection. In such a casethe stimulant is a lymphocyte that is non-syngeneic with respect to thecell.

Alternatively, the stimulant or inhibitor may be cell-free such as acell-free antigen (e.g., soluble antigen, virus, a cellular bioloigicalfluid). Specific examples of cell-free stimulants or inhibitors,include, but are not limited to, metabolites, nutrients (e.g., glucose),mitogens, peptides, cytokines, hormones, vitamins, drugs, antibodies,neurotransmitters, cancer specific antigens and variousdisease-associated tissue-specific normal antigens (TNAs).

Specific examples of MHC-restricted antigens (peptides) include but arenot limited to CEA (Carcinoembryonic Antigen), MUC-1, HER2, CD340, MAGEand prolactin (others are listed in Renkvist et al. 2001 “A listing ofhuman tumor antigens recognized by T cells”. Cancer Immunol.Immunotherapy 50:3-15.

The stimulant or inhibitor is contacted with the cell at variousconcentrations.

The stimulant or inhibitor is selected according to the suspectedpathology. For example, in in-vitro fertilization applications theongoing MA of the embryo secretions to it's extracellular fluid areexamined. Alternatively or additionally, the MA stimulation profiles ofthe mother PBLs are examined by stimulation of the embryo secretions.

In screening tests, the cells are contacted with a plurality ofstimulants or inhibitors, and the acidification profiles areconcomitantly monitored for each such reaction.

Thus, the MA test, as described above can be performed on a limitednumber of samples (e.g., using a tissue culture dish) or on a pluralityof samples, screening response to a plurality of stimulants/inhibitorsor screening a plurality of samples from different patients or acombination of same. High throughput screening can be performed using amulti well plate, a multi well plate reader (for detecting thefluorescent signal), a CCD camera applying image analysis or fiberoptics matrices.

According to an embodiment of the invention, the resultant acidificationprofiles are recorded and stored in a database such as on a computerreadable medium so as to enable data manipulation and construction ofcomputational models. As used herein, “computer readable medium” refersto any medium which can be read and accessed directly by a computer.Such media include, but are not limited to, magnetic storage media, suchas floppy discs, hard disc storage medium, and magnetic tape; opticalstorage media such as optical discs or CD-ROM; electrical storage mediasuch as RAM and ROM; and hybrids of these categories such asmagnetic/optical storage media. Selection and use of appropriate storagemedia is well within the capabilities of one of ordinary skill in theart.

As used herein, “recorded” refers to a process of storing information oncomputer readable medium.

The robustness and accurateness of the present methodology suggests itsuse in numerous clinical applications.

Thus, according to an aspect of the invention there is provided a methodof diagnosing a disease associated with a modified metabolic activity ina subject-in-need thereof, the method comprising:

(a) providing a biological sample of the subject which comprises a cell;(b) independently measuring in an extracellular environment of the celltime-dependent acidification profiles due to secretion of:(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products; and(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity of the cell and whereas a shift inof the metabolic activity compared to that of a normal unaffected cellsample examined under identical conditions is indicative of a diseaseassociated with modified metabolic activity.

The subject may be a healthy animal or a human subject undergoing aroutine well-being check up. Alternatively, the subject may be at riskof having a disease associated with a modified metabolic activity suchas cancer (e.g., a genetically predisposed subject, a subject withmedical and/or family history of cancer, a subject who has been exposedto carcinogens, occupational hazard, environmental hazard) and/or asubject who exhibits suspicious clinical signs of cancer [e.g., blood inthe stool or melena, unexplained pain, sweating, unexplained fever,unexplained loss of weight up to anorexia, changes in bowel habits(constipation and/or diarrhea), tenesmus (sense of incompletedefecation, for rectal cancer specifically), anemia and/or generalweakness).

As used herein “a disease associated with a modified metabolic activity”refers to a disease that is characterized by a cell population that hasundergone a shift in metabolic activity as compared to an identical cellpopulation taken from a normal, healthy (unaffected with the disease).That cell population that has undergone a shift in metabolic activity,can be a pathogenic cell population (i.e., disease-causing cells e.g.,cancer cells) or a non-pathogenic cell population (e.g., diseasecombating cells e.g., immune cells such as in the case of solid-tumor).For instance, as described hereinabove, in oncology, most cancer cellspredominantly and some populations of the immune system undergoingclonal expansion produce energy by a high rate of glycolysis followed bylactic acid production in the cytosol, rather than by a comparativelylow rate of glycolysis followed by oxidation of pyruvate in mitochondrialike most normal cells (see FIG. 1).

Cellular biological samples which can be used in accordance with thepresent teachings include, but are not limited to, blood (e.g.,peripheral blood leukocytes, peripheral blood mononuclear cells, wholeblood, cord blood), a solid tissue biopsy, cerebrospinal fluid, urine,lymph fluids, and various external secretions of the respiratory,intestinal and genitourinary tracts, synovial fluid, amniotic fluid andchorionic villi.

Biopsies include, but are not limited to, surgical biopsies includingincisional or excisional biopsy, fine needle aspirates and the like,complete resections or body fluids. Methods of biopsy retrieval are wellknown in the art.

As used herein the term “diagnosis” or “diagnosing” refers todetermining presence or absence of a pathology (e.g., a disease,disorder, condition or syndrome), classifying a pathology or a symptom,determining a severity of the pathology, monitoring pathologyprogression, forecasting an outcome of a pathology and/or prospects ofrecovery and screening of a subject for a specific disease.

According to the instant teachings the acidification profiles of anormal, healthy (unaffected) sample of identical cell composition aredetermined under identical conditions which were used to monitor thecells of the subject.

Once acidification profiles are obtained (e.g., with or withoutstimulant/inhibitor), the profile(s) are recorded. A shift (i.e., achange) in the metabolic activity between the cells of the subject andthose of the control (normal, unaffected), as evidenced from theacidification profiles obtained under identical conditions, isindicative of a disease associated with the modified metabolic activityprofiles.

The results of the metabolic activity assay may be subject to decisiontree models which classify the results and assist in final diagnosis.According to a preferred embodiment, at least two models are combined(see FIGS. 9 & 10). Examples of such models include, but are not limitedto, CHAID, C5 and C&R Tree. The Logistic model may be further applied.

Examples of medical conditions which can be diagnosed and treated (as isfurther described hereinbelow) according to the present teachingsinclude, but are not limited to, cancer, pathogenic infection andautoimmune diseases. Specific examples are provided infra.

Inflammatory Diseases

Include, but are not limited to, chronic inflammatory diseases and acuteinflammatory diseases.

Inflammatory Diseases Associated with Hypersensitivity

Examples of hypersensitivity include, but are not limited to, Type Ihypersensitivity, Type II hypersensitivity, Type III hypersensitivity,Type IV hypersensitivity, immediate hypersensitivity, antibody mediatedhypersensitivity, immune complex mediated hypersensitivity, T lymphocytemediated hypersensitivity and DTH.

Type I or immediate hypersensitivity, such as asthma.

Type II hypersensitivity include, but are not limited to, rheumatoiddiseases, rheumatoid autoimmune diseases, rheumatoid arthritis (Krenn V.et al., Histol Histopathol 2000 July; 15 (3):791), spondylitis,ankylosing spondylitis (Jan Voswinkel et al., Arthritis Res 2001; 3 (3):189), systemic diseases, systemic autoimmune diseases, systemic lupuserythematosus (Erikson J. et al., Immunol Res 1998; 17 (1-2):49),sclerosis, systemic sclerosis (Renaudineau Y. et al., Clin Diagn LabImmunol. 1999 March; 6 (2):156); Chan O T. et al., Immunol Rev 1999June; 169:107), glandular diseases, glandular autoimmune diseases,pancreatic autoimmune diseases, diabetes, Type I diabetes (Zimmet P.Diabetes Res Clin Pract 1996 October; 34 Suppl:S125), thyroid diseases,autoimmune thyroid diseases, Graves' disease (Orgiazzi J. EndocrinolMetab Clin North Am 2000 June; 29 (2):339), thyroiditis, spontaneousautoimmune thyroiditis (Braley-Mullen H. and Yu S, J Immunol 2000 Dec.15; 165 (12):7262), Hashimoto's thyroiditis (Toyoda N. et al., NipponRinsho 1999 August; 57 (8):1810), myxedema, idiopathic myxedema (MitsumaT. Nippon Rinsho. 1999 August; 57 (8):1759); autoimmune reproductivediseases, ovarian diseases, ovarian autoimmunity (Garza K M. et al., JReprod Immunol 1998 February; 37 (2):87), autoimmune anti-sperminfertility (Diekman A B. et al., Am J Reprod Immunol. 2000 March; 43(3):134), repeated fetal loss (Tincani A. et al., Lupus 1998; 7 Suppl2:S107-9), neurodegenerative diseases, neurological diseases,neurological autoimmune diseases, multiple sclerosis (Cross A H. et al.,J Neuroimmunol 2001 January 1; 112 (1-2):1), Alzheimer's disease (OronL. et al., J Neural Transm Suppl. 1997; 49:77), myasthenia gravis(Infante A J. And Kraig E, Int Rev Immunol 1999; 18 (1-2):83), motorneuropathies (Kornberg A J. J Clin Neurosci. 2000 May; 7 (3):191),Guillain-Barre syndrome, neuropathies and autoimmune neuropathies(Kusunoki S. Am J Med Sci. 2000 April; 319 (4):234), myasthenicdiseases, Lambert-Eaton myasthenic syndrome (Takamori M. Am J Med Sci.2000 April; 319 (4):204), paraneoplastic neurological diseases,cerebellar atrophy, paraneoplastic cerebellar atrophy,non-paraneoplastic stiff man syndrome, cerebellar atrophies, progressivecerebellar atrophies, encephalitis, Rasmussen's encephalitis,amyotrophic lateral sclerosis, Sydeham chorea, Gilles de la Tourettesyndrome, polyendocrinopathies, autoimmune polyendocrinopathies (AntoineJ C. and Honnorat J. Rev Neurol (Paris) 2000 January; 156 (1):23);neuropathies, dysimmune neuropathies (Nobile-Orazio E. et al.,Electroencephalogr Clin Neurophysiol Suppl 1999; 50:419); neuromyotonia,acquired neuromyotonia, arthrogryposis multiplex congenita (Vincent A.et al., Ann N Y Acad Sci. 1998 May 13; 841:482), cardiovasculardiseases, cardiovascular autoimmune diseases, atherosclerosis (MatsuuraE. et al., Lupus. 1998; 7 Suppl 2:S135), myocardial infarction (VaaralaO. Lupus. 1998; 7 Suppl 2:S132), thrombosis (Tincani A. et al., Lupus1998; 7 Suppl 2:S107-9), granulomatosis, Wegener's granulomatosis,arteritis, Takayasu's arteritis and Kawasaki syndrome (Praprotnik S. etal., Wien Klin Wochenschr 2000 Aug. 25; 112 (15-16):660); anti-factorVIII autoimmune disease (Lacroix-Desmazes S. et al., Semin ThrombHemost. 2000; 26 (2):157); vasculitises, necrotizing small vesselvasculitises, microscopic polyangiitis, Churg and Strauss syndrome,glomerulonephritis, pauci-immune focal necrotizing glomerulonephritis,crescentic glomerulonephritis (Noel L H. Ann Med Interne (Paris). 2000May; 151 (3):178); antiphospholipid syndrome (Flamholz R. et al., J ClinApheresis 1999; 14 (4):171); heart failure, agonist-like β-adrenoceptorantibodies in heart failure (Wallukat G. et al., Am J Cardiol. 1999 Jun.17; 83 (12A):75H), thrombocytopenic purpura (Moccia F. Ann Ital Med Int.1999 April-June; 14 (2):114); hemolytic anemia, autoimmune hemolyticanemia (Efremov D G. et al., Leuk Lymphoma 1998 January; 28 (3-4):285),gastrointestinal diseases, autoimmune diseases of the gastrointestinaltract, intestinal diseases, chronic inflammatory intestinal disease(Garcia Herola A. et al., Gastroenterol Hepatol. 2000 January; 23(1):16), celiac disease (Landau Y E. and Shoenfeld Y. Harefuah 2000January 16; 138 (2):122), autoimmune diseases of the musculature,myositis, autoimmune myositis, Sjogren's syndrome (Feist E. et al., IntArch Allergy Immunol 2000 September; 123 (1):92); smooth muscleautoimmune disease (Zauli D. et al., Biomed Pharmacother 1999 June; 53(5-6):234), hepatic diseases, hepatic autoimmune diseases, autoimmunehepatitis (Manns M P. J Hepatol 2000 August; 33 (2):326) and primarybiliary cirrhosis (Strassburg C P. et al., Eur J Gastroenterol Hepatol.1999 June; 11 (6):595).

Type IV or T cell mediated hypersensitivity, include, but are notlimited to, rheumatoid diseases, rheumatoid arthritis (Tisch R, McDevittH O. Proc Natl Acad Sci USA 1994 January 18; 91 (2):437), systemicdiseases, systemic autoimmune diseases, systemic lupus erythematosus(Datta S K., Lupus 1998; 7 (9):591), glandular diseases, glandularautoimmune diseases, pancreatic diseases, pancreatic autoimmunediseases, Type 1 diabetes (Castano L. and Eisenbarth G S. Ann. Rev.Immunol. 8:647); thyroid diseases, autoimmune thyroid diseases, Graves'disease (Sakata S. et al., Mol Cell Endocrinol 1993 March; 92 (1):77);ovarian diseases (Garza K M. et al., J Reprod Immunol 1998 February; 37(2):87), prostatitis, autoimmune prostatitis (Alexander R B. et al.,Urology 1997 December; 50 (6):893), polyglandular syndrome, autoimmunepolyglandular syndrome, Type I autoimmune polyglandular syndrome (HaraT. et al., Blood. 1991 Mar. 1; 77 (5):1127), neurological diseases,autoimmune neurological diseases, multiple sclerosis, neuritis, opticneuritis (Soderstrom M. et al., J Neurol Neurosurg Psychiatry 1994 May;57 (5):544), myasthenia gravis (Oshima M. et al., Eur J Immunol 1990December; 20 (12):2563), stiff-man syndrome (Hiemstra H S. et al., ProcNatl Acad Sci USA 2001 Mar. 27; 98 (7):3988), cardiovascular diseases,cardiac autoimmunity in Chagas' disease (Cunha-Neto E. et al., J ClinInvest 1996 Oct. 15; 98 (8):1709), autoimmune thrombocytopenic purpura(Semple J W. et al., Blood 1996 May 15; 87 (10):4245), anti-helper Tlymphocyte autoimmunity (Caporossi A P. et al., Viral Immunol 1998; 11(1):9), hemolytic anemia (Sallah S. et al., Ann Hematol 1997 March; 74(3):139), hepatic diseases, hepatic autoimmune diseases, hepatitis,chronic active hepatitis (Franco A. et al., Clin Immunol Immunopathol1990 March; 54 (3):382), biliary cirrhosis, primary biliary cirrhosis(Jones D E. Clin Sci (Colch) 1996 November; 91 (5):551), nephricdiseases, nephric autoimmune diseases, nephritis, interstitial nephritis(Kelly C J. J Am Soc Nephrol 1990 August; 1 (2):140), connective tissuediseases, ear diseases, autoimmune connective tissue diseases,autoimmune ear disease (Yoo T J. et al., Cell Immunol 1994 August; 157(1):249), disease of the inner ear (Gloddek B. et al., Ann N Y Acad Sci1997 Dec. 29; 830:266), skin diseases, cutaneous diseases, dermaldiseases, bullous skin diseases, pemphigus vulgaris, bullous pemphigoidand pemphigus foliaceus.

Examples of delayed type hypersensitivity include, but are not limitedto, contact dermatitis and drug eruption.

Examples of types of T lymphocyte mediating hypersensitivity include,but are not limited to, helper T lymphocytes and cytotoxic Tlymphocytes.

Examples of helper T lymphocyte-mediated hypersensitivity include, butare not limited to, T_(h)1 lymphocyte mediated hypersensitivity andT_(h)2 lymphocyte mediated hypersensitivity.

Autoimmune Diseases

Include, but are not limited to, cardiovascular diseases, rheumatoiddiseases, glandular diseases, gastrointestinal diseases, cutaneousdiseases, hepatic diseases, neurological diseases, muscular diseases,nephric diseases, diseases related to reproduction, connective tissuediseases and systemic diseases.

Examples of autoimmune cardiovascular diseases include, but are notlimited to atherosclerosis (Matsuura E. et al., Lupus. 1998; 7 Suppl2:S135), myocardial infarction (Vaarala O. Lupus. 1998; 7 Suppl 2:S132),thrombosis (Tincani A. et al., Lupus 1998; 7 Suppl 2:S107-9), Wegener'sgranulomatosis, Takayasu's arteritis, Kawasaki syndrome (Praprotnik S.et al., Wien Klin Wochenschr 2000 Aug. 25; 112 (15-16):660), anti-factorVIII autoimmune disease (Lacroix-Desmazes S. et al., Semin ThrombHemost. 2000; 26 (2):157), necrotizing small vessel vasculitis,microscopic polyangiitis, Churg and Strauss syndrome, pauci-immune focalnecrotizing and crescentic glomerulonephritis (Noel L H. Ann Med Interne(Paris). 2000 May; 151 (3):178), antiphospholipid syndrome (Flamholz R.et al., J Clin Apheresis 1999; 14 (4):171), antibody-induced heartfailure (Wallukat G. et al., Am J Cardiol. 1999 Jun. 17; 83 (12A):75H),thrombocytopenic purpura (Moccia F. Ann Ital Med Int. 1999 April-June;14 (2):114; Semple J W. et al., Blood 1996 May 15; 87 (10):4245),autoimmune hemolytic anemia (Efremov D G. et al., Leuk Lymphoma 1998January; 28 (3-4):285; Sallah S. et al., Ann Hematol 1997 March; 74(3):139), cardiac autoimmunity in Chagas' disease (Cunha-Neto E. et al.,J Clin Invest 1996 Oct. 15; 98 (8):1709) and anti-helper T lymphocyteautoimmunity (Caporossi A P. et al., Viral Immunol 1998; 11 (1):9).

Examples of autoimmune rheumatoid diseases include, but are not limitedto rheumatoid arthritis (Krenn V. et al., Histol Histopathol 2000 July;15 (3):791; Tisch R, McDevitt H O. Proc Natl Acad Sci units S A 1994January 18; 91 (2):437) and ankylosing spondylitis (Jan Voswinkel etal., Arthritis Res 2001; 3 (3): 189).

Examples of autoimmune glandular diseases include, but are not limitedto, pancreatic disease, Type I diabetes, thyroid disease, Graves'disease, thyroiditis, spontaneous autoimmune thyroiditis, Hashimoto'sthyroiditis, idiopathic myxedema, ovarian autoimmunity, autoimmuneanti-sperm infertility, autoimmune prostatitis and Type I autoimmunepolyglandular syndrome. diseases include, but are not limited toautoimmune diseases of the pancreas, Type 1 diabetes (Castano L. andEisenbarth G S. Ann. Rev. Immunol. 8:647; Zimmet P. Diabetes Res ClinPract 1996 October; 34 Suppl:S125), autoimmune thyroid diseases, Graves'disease (Orgiazzi J. Endocrinol Metab Clin North Am 2000 June; 29(2):339; Sakata S. et al., Mol Cell Endocrinol 1993 March; 92 (1):77),spontaneous autoimmune thyroiditis (Braley-Mullen H. and Yu S, J Immunol2000 Dec. 15; 165 (12):7262), Hashimoto's thyroiditis (Toyoda N. et al.,Nippon Rinsho 1999 August; 57 (8):1810), idiopathic myxedema (Mitsuma T.Nippon Rinsho. 1999 August; 57 (8):1759), ovarian autoimmunity (Garza KM. et al., J Reprod Immunol 1998 February; 37 (2):87), autoimmuneanti-sperm infertility (Diekman A B. et al., Am J Reprod Immunol. 2000March; 43 (3):134), autoimmune prostatitis (Alexander R B. et al.,Urology 1997 December; 50 (6):893) and Type I autoimmune polyglandularsyndrome (Hara T. et al., Blood. 1991 Mar. 1; 77 (5):1127).

Examples of autoimmune gastrointestinal diseases include, but are notlimited to, chronic inflammatory intestinal diseases (Garcia Herola A.et al., Gastroenterol Hepatol. 2000 January; 23 (1):16), celiac disease(Landau Y E. and Shoenfeld Y. Harefuah 2000 Jan. 16; 138 (2):122),colitis, ileitis and Crohn's disease.

Examples of autoimmune cutaneous diseases include, but are not limitedto, autoimmune bullous skin diseases, such as, but are not limited to,pemphigus vulgaris, bullous pemphigoid and pemphigus foliaceus.

Examples of autoimmune hepatic diseases include, but are not limited to,hepatitis, autoimmune chronic active hepatitis (Franco A. et al., ClinImmunol Immunopathol 1990 March; 54 (3):382), primary biliary cirrhosis(Jones D E. Clin Sci (Colch) 1996 November; 91 (5):551; Strassburg C P.et al., Eur J Gastroenterol Hepatol. 1999 June; 11 (6):595) andautoimmune hepatitis (Manns M P. J Hepatol 2000 August; 33 (2):326).

Examples of autoimmune neurological diseases include, but are notlimited to, multiple sclerosis (Cross A H. et al., J Neuroimmunol 2001Jan. 1; 112 (1-2):1), Alzheimer's disease (Oron L. et al., J NeuralTransm Suppl. 1997; 49:77), myasthenia gravis (Infante A J. And Kraig E,Int Rev Immunol 1999; 18 (1-2):83; Oshima M. et al., Eur J Immunol 1990December; 20 (12):2563), neuropathies, motor neuropathies (Kornberg A J.J Clin Neurosci. 2000 May; 7 (3):191); Guillain-Barre syndrome andautoimmune neuropathies (Kusunoki S. Am J Med Sci. 2000 April; 319(4):234), myasthenia, Lambert-Eaton myasthenic syndrome (Takamori M. AmJ Med Sci. 2000 April; 319 (4):204); paraneoplastic neurologicaldiseases, cerebellar atrophy, paraneoplastic cerebellar atrophy andstiff-man syndrome (Hiemstra H S. et al., Proc Natl Acad Sci units S A2001 Mar. 27; 98 (7):3988); non-paraneoplastic stiff man syndrome,progressive cerebellar atrophies, encephalitis, Rasmussen'sencephalitis, amyotrophic lateral sclerosis, Sydeham chorea, Gilles dela Tourette syndrome and autoimmune polyendocrinopathies (Antoine J C.and Honnorat J. Rev Neurol (Paris) 2000 January; 156 (1):23); dysimmuneneuropathies (Nobile-Orazio E. et al., Electroencephalogr ClinNeurophysiol Suppl 1999; 50:419); acquired neuromyotonia, arthrogryposismultiplex congenita (Vincent A. et al., Ann N Y Acad Sci. 1998 May 13;841:482), neuritis, optic neuritis (Soderstrom M. et al., J NeurolNeurosurg Psychiatry 1994 May; 57 (5):544) and neurodegenerativediseases.

Examples of autoimmune muscular diseases include, but are not limitedto, myositis, autoimmune myositis and primary Sjogren's syndrome (FeistE. et al., Int Arch Allergy Immunol 2000 September; 123 (1):92) andsmooth muscle autoimmune disease (Zauli D. et al., Biomed Pharmacother1999 June; 53 (5-6):234).

Examples of autoimmune nephric diseases include, but are not limited to,nephritis and autoimmune interstitial nephritis (Kelly C J. J Am SocNephrol 1990 August; 1 (2):140).

Examples of autoimmune diseases related to reproduction include, but arenot limited to, repeated fetal loss (Tincani A. et al., Lupus 1998; 7Suppl 2:S107-9).

Examples of autoimmune connective tissue diseases include, but are notlimited to, ear diseases, autoimmune ear diseases (Yoo T J. et al., CellImmunol 1994 August; 157 (1):249) and autoimmune diseases of the innerear (Gloddek B. et al., Ann N Y Acad Sci 1997 Dec. 29; 830:266).

Examples of autoimmune systemic diseases include, but are not limitedto, systemic lupus erythematosus (Erikson J. et al., Immunol Res 1998;17 (1-2):49) and systemic sclerosis (Renaudineau Y. et al., Clin DiagnLab Immunol. 1999 March; 6 (2):156); Chan O T. et al., Immunol Rev 1999June; 169:107).

Infectious Diseases

Examples of infectious diseases include, but are not limited to, chronicinfectious diseases, subacute infectious diseases, acute infectiousdiseases, viral diseases, bacterial diseases, protozoan diseases,parasitic diseases, fungal diseases, mycoplasma diseases and priondiseases.

Graft Rejection Diseases

Examples of diseases associated with transplantation of a graft include,but are not limited to, graft rejection, chronic graft rejection,subacute graft rejection, hyperacute graft rejection, acute graftrejection and graft versus host disease.

Allergic Diseases

Examples of allergic diseases include, but are not limited to, asthma,hives, urticaria, pollen allergy, dust mite allergy, venom allergy,cosmetics allergy, latex allergy, chemical allergy, drug allergy, insectbite allergy, animal dander allergy, stinging plant allergy, poison ivyallergy and food allergy.

According to a specific embodiment the disease is cancer.

Cancerous Diseases

Examples of cancer include but are not limited to carcinoma, lymphoma,blastoma, sarcoma, and leukemia. Particular examples of cancerousdiseases but are not limited to: Myeloid leukemia such as Chronicmyelogenous leukemia. Acute myelogenous leukemia with maturation. Acutepromyelocytic leukemia, Acute nonlymphocytic leukemia with increasedbasophils, Acute monocytic leukemia. Acute myelomonocytic leukemia witheosinophilia; Malignant lymphoma, such as Birkitt's Non-Hodgkin's;Lymphoctyic leukemia, such as Acute lumphoblastic leukemia. Chroniclymphocytic leukemia; Myeloproliferative diseases, such as Solid tumorsBenign Meningioma, Mixed tumors of salivary gland, Colonic adenomas;Adenocarcinomas, such as Small cell lung cancer, Kidney, Uterus,Prostate, Bladder, Ovary, Colon, Sarcomas, Liposarcoma, myxoid, Synovialsarcoma, Rhabdomyosarcoma (alveolar), Extraskeletel myxoidchonodrosarcoma, Ewing's tumor; other include Testicular and ovariandysgerminoma, Retinoblastoma, Wilms' tumor, Neuroblastoma, Malignantmelanoma, Mesothelioma, breast, skin, paccreas, cervix, prostate, andovarian.

Thus, the present teachings can be used in disease detection. Followingis a non-limiting embodiment which relates to early cancer detection.

The present teachings provide for an immune system-based approach as anon-invasive diagnostic tool for early detection and staging of cancer.Conventional diagnostic approaches are mainly focused on the pathologyof malignant tissues and on cancer specific antigens and genes. Incontrary, the present teachings focus on normal versus abnormalresponses of the immune system as a naturally available tool for earlydetection and staging of cancer (as well as of other diseases). Thus,the present teachings provide for a high throughput functionalphysiological blood test by measurement of PBLs metabolic activitystimulation profiles (MASPs) of the immune system in response to a widespectrum of stimulants/inhibitors at different concentrations(metabolites, nutrients, mitogens, natural and synthetic peptides,cytokines, hormones, vitamins, drugs, antibodies, neurotransmitters,cancer specific antigens and various disease-associated tissue-specificnormal antigens (TNAs)). By conventional views of immunologicalresponses relatively small effects are anticipated, even under “clonalexpansion” of effector subpopulations of PBLs. However, in terms of“systems biology”, a minor subgroup response may be amplified throughoutnetwork stimulation. It is suggested that the immune system combatscancer by its regular function in detecting abnormal high levels ofTNAs. PBLs metabolic activity may also be used for diagnosis of advancedstages of cancer development. At the stage of local tumor, the effectivekilling response of the immune system is already limited, being unableto destroy the tumor tissue even though specific tumor infiltratinglymphocytes (TILs) are observed. Yet, in this stage such circulating Tlymphocytes might be still responsible for killing separate circulatingcancer cells. The shift from a local tumor to a metastatic-phase pointson a complete specific failure of the immune system, and this transitionmight be measured by characteristic changes in the MA stimulationprofiles.

Disease diagnosis made according to the present teachings is followed bysubstantiation of the screen results using gold standard methods. Oncediagnosis is established either relying on the present teachings orsubstantiated using Gold standard methods, the subject is informed ofthe diagnosis and treated as needed.

It will be appreciated that the present teachings have a variety ofapplications pertaining to individually optimizing disease treatment,monitoring disease treatment in a subject, determining a treatment for asubject and identifying an agent capable of treating a diseaseassociated with abnormal metabolic activity.

Thus, according to an aspect of the invention there is provided a methodof disease treatment in a subject in need thereof, the methodcomprising:

(a) diagnosing a presence of the disease in the subject according to themethod described above; and

(b) treating the subject based on the diagnosis.

As used herein the term “treating” refers to abrogating, substantiallyinhibiting, slowing or reversing the progression of a condition,substantially ameliorating clinical or aesthetical symptoms of acondition or substantially preventing the appearance of clinical oraesthetical symptoms of a condition.

According to another aspect of the invention there is provided a methodof individually optimizing disease treatment, the method comprising:

(a) contacting a biological sample of the subject which comprises a cellwith at least one medicament;(b) independently measuring in an extracellular environment of the celltime-dependent acidification profiles due to secretion of:(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products; and(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity of the cell and whereas a shift inthe metabolic activity of the cell towards that of a normal healthy cellsample examined under identical conditions is indicative of anefficacious medicament for the disease.

As used herein “individually optimizing treatment” refers to an ex vivomethod of tailoring treatment regimen (e.g., type of medicament, dose).

As used herein a “medicament” refers to a formulation of a medicine,medicinal drug or medication, as interchangeably used herein. Examplesof medicaments, include but are not limited to, chemotherapy,antibiotics, antiparasitic drugs, antiviral and the like.

As used herein, the term “contacting” refers to bringing the medicamentinto the vicinity of a cell under conditions such that the medicamentcontacts the cell membrane and if needed internalizes thereto. Thus, forexample, the contacting should be effected under buffer conditions, at atemperature and time sufficient to allow the medicament to affect cellphenotype (e.g., cytotoxic or cytostatic effect). The contacting may beeffected in vitro, ex vivo or in vivo. The contacting may be effected ina vessel which is also capable of detecting the product of the enzymaticreaction (i.e., in the electrochemical cell), such that the electricalsignal is detected on-line. Such vessels are further described hereinbelow. Alternatively, the contacting may be effected in a separatevessel from where the detection takes place such that it is possible tocontinuously withdraw samples at particular time points and place suchsamples within the electrochemical cells. Thus, the contacting may beeffected in a test tube, flask, tissue culture, chip, array, plate,microplate, capillary, or the like. The cells may be placed on avibrating plate following the addition of the substrate for continuousthorough mixing of the contents of the cells.

According to a specific embodiment, “a shift in the metabolic activityof the cell towards that of a normal healthy cell sample examined underidentical conditions” refers to at least a 10% local or global(throughout the profile) shift preferably towards 100% identity to thecontrol normal healthy cell sample.

A shift beyond a predetermined threshold as will be determined by theskilled artisan is indicative of an efficacious treatment.

Likewise, there is provided a method of monitoring disease treatment ina subject, the method comprising:

(a) administering at least one medicament against the disease to thesubject;(b) retrieving a biological sample which comprises a cell of the subjectfollowing the administering;(c) independently measuring in an extracellular environment of the celltime-dependent acidification profiles due to secretion of:(i) non-volatile soluble metabolic products;(ii) non-volatile soluble metabolic products and volatile solublemetabolic products; and(iii) volatile soluble metabolic products;wherein at least one of the time dependent acidification profiles isindicative of the metabolic activity of the cell and whereas a shift inthe metabolic activity of the cells towards that of a normal healthycell sample examined under identical conditions is indicative of anefficacious treatment of the disease. For example, it is suggested thatin the metastatic phase the MA profile might regress close to the normalprofile.

Likewise, there is provided a method of identifying an agent capable ofaltering a metabolic activity of cells, the method comprising:

(a) subjecting cells to an agent;

(b) measuring the metabolic activity of the cells following (a) andoptionally prior to (a) according to the method of claim 1, wherein ashift in the acidification profiles is indicative of an agent capable ofaltering a metabolic activity of cells.

As used herein, the term “agent” refers to a test composition comprisinga biological agent or a chemical agent.

Examples of biological agents that may be tested as potential modulatorsof metabolic activity according to the method of the present inventioninclude, but are not limited to, nucleic acids, e.g., polynucleotides,ribozymes, siRNA and antisense molecules (including without limitationRNA, DNA, RNA/DNA hybrids, peptide nucleic acids, and polynucleotideanalogs having altered backbone structures or other chemicalmodifications); proteins, polypeptides (e.g. peptides), carbohydrates,lipids and “small molecule” drug candidates. “Small molecules” can be,for example, naturally occurring compounds (e.g., compounds derived fromplant extracts, microbial broths, and the like) or synthetic organic ororganometallic compounds having molecular weights of less than about10,000 daltons, preferably less than about 5,000 daltons, and mostpreferably less than about 1,500 daltons.

According to a preferred embodiment of this aspect of the presentinvention the agents are anti-cancer, anti-viral or antibiotic agents.

Examples of conditions that may be tested as potential anti canceragents according to the method of the present invention include, but arenot limited to, radiation exposure (such as, gamma radiation, UVradiation, X-radiation).

It will be appreciated that the shift, as used herein, can be also adifferent level (e.g., higher level) of MA in same profile; a change inbasal state, and/or a shift in the agent concentration that inducesmaximal MA effect.

Once an agent capable of altering a metabolic activity of cells has beenidentified along in accordance with the above teachings, the inventionfurther comprises synthesizing the agent or purifying it from a naturalsource.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

As used herein the term “method” refers to manners, means, techniquesand procedures for accomplishing a given task including, but not limitedto, those manners, means, techniques and procedures either known to, orreadily developed from known manners, means, techniques and proceduresby practitioners of the chemical, pharmacological, biological,biochemical and medical arts.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Various embodiments and aspects of the present invention as delineatedhereinabove and as claimed in the claims section below find experimentalsupport in the following examples.

EXAMPLES

Reference is now made to the following examples, which together with theabove descriptions illustrate some embodiments of the invention in a nonlimiting fashion.

Generally, the nomenclature used herein and the laboratory proceduresutilized in the present invention include molecular, biochemical,microbiological and recombinant DNA techniques. Such techniques arethoroughly explained in the literature. See, for example, “MolecularCloning: A laboratory Manual” Sambrook et al., (1989); “CurrentProtocols in Molecular Biology” Volumes I-III Ausubel, R. M., ed.(1994); Ausubel et al., “Current Protocols in Molecular Biology”, JohnWiley and Sons, Baltimore, Md. (1989); Perbal, “A Practical Guide toMolecular Cloning”, John Wiley & Sons, New York (1988); Watson et al.,“Recombinant DNA”, Scientific American Books, New York; Birren et al.(eds) “Genome Analysis: A Laboratory Manual Series”, Vols. 1-4, ColdSpring Harbor Laboratory Press, New York (1998); methodologies as setforth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and5,272,057; “Cell Biology: A Laboratory Handbook”, Volumes I-III Cellis,J. E., ed. (1994); “Current Protocols in Immunology” Volumes I-IIIColigan J. E., ed. (1994); Stites et al. (eds), “Basic and ClinicalImmunology” (8th Edition), Appleton & Lange, Norwalk, Conn. (1994);Mishell and Shiigi (eds), “Selected Methods in Cellular Immunology”, W.H. Freeman and Co., New York (1980); available immunoassays areextensively described in the patent and scientific literature, see, forexample, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578;3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533;3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and 5,281,521;“Oligonucleotide Synthesis” Gait, M. J., ed. (1984); “Nucleic AcidHybridization” Hames, B. D., and Higgins S. J., eds. (1985);“Transcription and Translation” Hames, B. D., and Higgins S. J., Eds.(1984); “Animal Cell Culture” Freshney, R. I., ed. (1986); “ImmobilizedCells and Enzymes” IRL Press, (1986); “A Practical Guide to MolecularCloning” Perbal, B., (1984) and “Methods in Enzymology” Vol. 1-317,Academic Press; “PCR Protocols: A Guide To Methods And Applications”,Academic Press, San Diego, Calif. (1990); Marshak et al., “Strategiesfor Protein Purification and Characterization—A Laboratory CourseManual” CSHL Press (1996); all of which are incorporated by reference asif fully set forth herein. Other general references are providedthroughout this document. The procedures therein are believed to be wellknown in the art and are provided for the convenience of the reader. Allthe information contained therein is incorporated herein by reference.

Example 1 Experimental Procedures

A. Blood Donors Requirement and Blood Collection by Medical Experts

Blood samples were collected in Vacutubes 9 ml with EDTA (GreinerBio-One 455036). The study was approved by the institutional ReviewBoards at the Sheba Medical Center (Ramat Gan Israel) and the IsraeliMinistry of Health, (Helsinki Approval number 7780-10-SMC).

B. Collection of Donor's Demographic and Clinical Information

For the protection of confidentiality, all collected blood samples werelabeled and immediately coded to be used in the database records anddiagnostic analysis.

The MA test results were collected from 42 healthy donors and 25 cancerpatients from 22 to 81 years old (Table 2). The healthy donors are amixed population including treated cases of high blood pressure, highcholesterol levels, minor flu and inflammation.

TABLE 2 Clinical characteristics of cancer patients: ac—adencocarcinoma,idc—invasive ductal carcinoma, gej—gastroesphageal junction,NSCLC—Non-small-cell lung carcinoma Tumor Stage Tumor Type Gender AgePatient Number 1 Breast idc Female 53 1 1 Breast idc Female 64 2 2Breast idc Male 63 3 2 Breast idc Female 37 4 2 Breast idc Female 61 5 2Breast idc Female 38 6 2 Breast idc Female 65 7 3 Breast idc Female 32 83 Breast idc Female 42 9 3 NSCLC ac Male 46 10 4 NSCLC ac Male 61 11 4NSCLC ac Female 60 12 4 NSCLC ac Male 81 13 4 Colon ac Female 70 14 4Colon Female 49 15 2 Colon ac Female 56 16 2 Rectum ac Female 52 17 4Rectum ac Male 74 18 3 Gastric ac Male 47 19 4 Gastric gej Male 64 20 3Pancreas Female 57 21 4 Pancreas Male 58 22 4 Prostate Male 77 23 1Thyroid Female 54 24 2 Cervix Female 35 25

C. Blood Samples Transport

Measures are taken to keep the viability of the blood cells atthermo-stated conditions (Thermo Electric cooling (down to 10° C.-18°C.) and gently shaking till the PBMCs separation.

D₁. Peripheral Blood Mononuclear Cells (PBMCs) Separation

Fresh peripheral blood mononuclear cells (PBMCs) were isolated byFicoll-Paque (UNI-SEP, Novamed) and gradient centrifugation. The pelletwas resuspended in working solution (WS) (PBS with calcium andmagnesium) including the fluorescent probe (HPTS) at a finalconcentration of 5×10⁶ cells/ml.

D₂. High Throughput Parallel Measurements of the MA Test

Each well in a black non-binding, low-volume 384 multi-well plate(Greiner Bio-One) was loaded with 10 μl of the PBMCs solution and 10 μlof working solution plus HPTS including one of ten reagents in 8increasing concentrations. Thus the final concentration of the probe ineach well was 1 μM, and the final concentration of the PBMCs was 2.5×10⁶cells/ml in 20 μl physiological working solution containing 10 mMphosphate buffer around pH 7.3. Taking into account the average PBMCsconcentration in adult peripheral blood a working concentration of2.5×10⁶ cells/ml was selected. This concentration of PBMCs is consideredto ensure two aspects: first, to get a reasonable signal-to-noise ratiodue to product accumulation during at least 1 hour, and second, to allowfor intercellular interaction. The same protocol of 10 μl loading wascarried out at least in triplicates, first on the PBMCs samples and thenon the reagents, so as to accurately obtain the final 20 μl volume inthe required concentration in each well. Furthermore, in each test twotypes of controls were included: one including only the probe (1 μM),without cells and without the stimulants in 8 wells; the other oneincluding only cells without a stimulant, basal state, in 8 wells. Theacidification process was monitored each 5 min during 1 hour ofincubation at 37° C. by a commercial fluorescence scanner (TECANInfinite M200). First, the scanner monitored the acidification processwithout sealing (“OPEN” mode) during 30 min (6 cycles) and then, toavoid ventilation of CO₂ and NH₃ from each well, the multi-well platewas sealed hermetically (ThermalSeal RT™, EXCEL Scientific, Inc.)(“CLOSE” mode). Next, the acidification process was monitored againduring 30 min (6 cycles). In order to increase the signal to noiseratio, the fluorescence intensities at 513 nm were measured sequentiallyunder excitation at 455 nm and 403 nm per well.

D₃. Type, Spectrum and Preparation of Reagents

In each test, the metabolic activity profiles of PBMCs were monitored inthe basal state and under the influence of the following ten reagentsdiluted in working solution in 8 different concentrations: PHA, CONA,PMA, LPS, MBP(28), MelanA, PSA(29), Glucose(24), L-glutamine andRapamycin (Strauss L, Czystowska M, Szajnik M, Mandapathil M, &Whiteside T L (2009) Differential responses of human regulatory T cells(Treg) and effector T cells to rapamycin. PLoS One 4(6):e5994.). Thereagents selection was made by their relation to the immune system(TABLE 1, above, note, concentrations are not limited to those in thetable, concentration=0 to non toxic dose).

It should be mentioned that other reagents are under calibration, forexample: (hormones such as estradiol, cancer specific antigens such ascarcinoembryonic antigen (CEA), cytokines and chemokines such as il-2,vitamins, hormones, drugs, antibodies of the immune system,neurotransmitters, different cancer peptides and specific viruses ortheir fragments such as human papilomavirus (HPV) (data not shown).

D₄. Ratiometric Measurement of the pH Sensitive Fluorescent Probe and WSAcidity Calibration

The probe used in this test is 8-Hydroxypyrene-1,3,6-trisulfonic acid(HPTS).

HPTS is a cost effective, non-toxic, highly water-solublemembrane-impermeant pH indicator with a pKa of ˜7.3 in aqueous buffers.HPTS exhibits a pH-dependent absorption shift, allowing ratiometric pHmeasurements as function of the ratio between the fluorescenceintensities at 513 nm that are measured sequentially under excitation at455 nm and 403 nm. This method is essential for the present sensitivemeasurements of minor pH changes in the physiological range around pH 7.

In order to quantify the sensitivity of the probe, a stock solution ofHPTS was diluted in water to a concentration of 100 μM and then to afinal concentration of 1 μM and 10 μM. WS acidity calibration was madefor two buffer concentrations: the WS (10 mM phosphate buffer) and theWS diluted 5 times by saline (2 mM phosphate buffer) (FIGS. 3A-D, FIGS.4A-B).

The final calibration curve used in the MA test was carried out bypH-glass electrode measurements of sequential titration of WS(containing 2 μM HPTS). The pH measurements and the fluorescencemeasurements of the titrated samples are carried out at 37° C. Thesamples are loaded into a multi-well plate and the fluorescenceintensities under EX403 nm and EX455 nm are measured at 513 nm using thefluorescence scanner.

For the “OPEN” and “CLOSE” states, a calibration polynomial curve isconstructed (FIGS. 3C-D), allowing to measure pH values and accumulatedacidification equivalents as a function of the ratio between thefluorescence intensities measured at 513 nm, under excitations at 403 nmand 455 nm, respectively.

The equations obtained were used for analysis of every new donor (Table3, below).

TABLE 3 The equations obtained from the final calibration curves: X =Fluorescence intensity at Ex. 403 nm/Fluorescence Intensity at Ex. 455nm pH HCI (μmol/ml) Open Y = 0.011X⁴ − 0.136X³ + 0.641X² − 1.528X +8.266 Y = 0.006X⁴ + 0.129X³ − 0.982X² + 3.971X − 2.276 Close Y = 0.013X⁴− 0.164X³ + 0.747X² − 1.723X + 8.415 Y = −0.006X⁴ + 0.131X³ − 1.011X² +4.168X − 2.581

E. Data Analysis

Computation, analysis and data mining (Nisbet R, I V J E, & Miner G(2009) Handbook of Statistical Analysis and Data Mining Applications)were done by using the following Statistical Package; EXCEL 2007,OriginPro 8, SAS Edition 9.2, PASW Modular client 13.0 (formally calledClementine, part of SPSS). Results in FIGS. 6A-C-8A-D, 13 are expressedas means±standard error of the mean. Statistical significance betweenhealthy and cancer patients for variant models was calculated usingchi-square. Results were considered statistically different when p<0.05.

Donor's Data Analysis (Summarized in Flowchart of the MA TestFramework—FIG. 12)

Data Preparation—

Step 0: Processing and Normalizing Donor Data

The raw data of each record (donor) was processed to yield the resultsin terms of the acidification rate of metabolic activity in units ofpmole H⁺/μl/hour/2500PBMCs.

Pre Processing—Step 1a: Probe Analysis and Donor Data Normalization

In order to improve the signal to noise ratio, analysis of probe wasdone by performing k-means cluster analysis on all observations (n=730)collected from all donors (FIGS. 5A-D). These processed results werenormalized by subtracting the donor values from the HPTS mean valuesafter removal of probe's outliers (not more than 5% of the results wereremoved).

Pre Processing—Step 1b: Excluded Outliers in Donor's Data

Normalizing “OPEN” and “CLOSE” values by the mean values of “OPEN” and“CLOSE” for each combination of donor, dose and stimulant. Observationswith standard score>|1.7| were discarded (1.77% of the results).

Data Preparation—Step 1c: Representation of Donor Metabolic ActivityResults

After removing the outliers in each donor, average values of “OPEN” and“CLOSE” were calculated separately for each donor based on the averageof at least triplicate results for each dose and each reagent per donor.These results would be used later as representing donor metabolicactivity for each reagent and dose. The results were planted in 2Dgraphs and 3D graphs and would be updated automatically with everydonor.

Searching for Classification Model—Step 2: Data Mining Algorithm

Since most of the studied cancer patients were older than 39 and inorder to minimize as much as possible the effect of age, two cohorts ofblood donors including males and females were tested and analyzed. Thefirst cohort includes donors with the age above 40 (n=42 (21 healthydonors and 21 cancer patients)) and the second cohort include the fullset of donors (n=67 (42 healthy donors and 25 cancer patients)) from22-81 years old. For Classification of the step 3 results a set ofalgorithms from a family of Decision Trees/Rule Induction (C5, CART,CHAID, ASSOCIATION RULE) and log linear model (Logistic Regression) wasused. Exploratory analysis methods were used to investigate hidden andunhidden connections in the data.

Model Evaluation—Step 3: Model Building and Classification

For classifying the donors into healthy and cancer patients, a set fromthe family of ten different models including data mining, machinelearning and statistical modeling were used applying SAS 9.3 andClementine software (V13.0). In order to evaluate and compare theperformance of the models it was decided to use the graphical methodwhich is based on the cumulative gain charts produce by Clementinesoftware (V13.0) (FIGS. 9A-D).

Predictive Modeling—Step 4: Evaluation Using a Validation Set of 30% ofBlood's Donors

Data as described in step 3 was randomly partitioned into two groups of“Training” and “Testing” using the Clementine software (V13.0). The“Training set” are used to build the data mining model and includes 70%of the donors. The remaining 30% of the donors will be used to evaluatethe classification result on the “Testing” set using the models thatwere generated in the Training set (FIGS. 10A-D).

The whole data analysis process is summarized in the Flowchart of the MAtest protocol and analysis Framework (FIG. 12).

Example 2 MA Test Design and Characteristics

Fresh peripheral blood mononuclear cells (hPBMCs) were isolated byFicoll-Paque and gradient centrifugation from 42 healthy donors and 25cancer patients (TABLE 2, above). For each blood sample, a 384multi-well plate was loaded with 20 μl containing physiological workingsolution at 10 mM buffer around pH 7.3, hPBMCs at final concentration of˜2.5×10⁶ cells/ml, 1 μM pH probe (HPTS), and one of ten stimulatingreagents in eight increasing concentrations (TABLE 3, above). The MAtest is carried out using a commercial fluorescence scanner. Theextracellular acidity kinetic profiles were measured either underair-open (“OPEN”) or hermetically-sealed closed (“CLOSE”) states. Bothrecords enable to measure the real-time accumulations of ‘soluble’versus ‘volatile’ metabolic products (lactic acid versus CO₂ and NH₃),thereby differentiating between oxidative phosphorylation, anaerobicglycolysis and aerobic glycolysis (“Warburg effect”)(Vander Heiden M G,Cantley L C, & Thompson C B (2009) Understanding the Warburg effect: themetabolic requirements of cell proliferation. Science324(5930):1029-1033). The MA rate profiles were calculated and examinedfor cancer diagnosis by dynamic online analysis, including data miningtools (FIG. 12).

Example 3 Ratiometric Fluorescence Extracellular pH Measurement andAcidity Calibration

The non-toxic, membrane-impermeant, ratiometric molecular pH-probe usedin the present MA test is 8-hydroxypyrene-1,3,6-trisulfonic acid (HPTS)(Hakonen A & Hulth S (2008) A high-precision ratiometric fluorosensorfor pH: implementing time-dependent non-linear calibration protocols fordrift compensation. Anal Chim Acta 606(1):63-71; Han J & Burgess K(Fluorescent indicators for intracellular pH. Chem Rev 110(5):2709-2728)with a pK_(a) of ˜7.3 in aqueous physiological buffers. It is well-knownby its low-toxicity, from intracellular pH measurements in many celltypes, even under overnight incubation at 2 mM (Overly C C, Lee K D,Berthiaume E, & Hollenbeck P J (1995) Quantitative measurement ofintraorganelle pH in the endosomal-lysosomal pathway in neurons by usingratiometric imaging with pyranine. Proc Natl Acad Sci USA92(8):3156-3160). Here, HPTS is rather used for extracellular pHmeasurements, at a low 1 μM concentration, which further ensure itsnon-toxicity. Calibration polynomial curves were constructed for the“OPEN” and “CLOSE” states, (FIGS. 3A-D), allowing to measure pH valuesand accumulated acidification equivalents as a function of the ratiobetween the fluorescence intensities measured at 513 nm, underexcitations at 403 nm and 455 nm, respectively. The acidificationcalibration curves were obtained for the working solution (WS) and forthe WS diluted 5 times with saline (10 mM and 2 mM phosphate buffer,respectively) (FIGS. 3A-D). As expected, this figure verifies that the10 mM buffer capacity allows for about five times of the acidificationvalues compared to that of the 2 mM buffer capacity, within the samerange of pH changes. These results indicate on the proper sensitivity toacidification within the physiological pH range 6.5-7.5. Further resultsindicate that the measurement method is independent of the fluorescentprobe concentration between 1-10 μM. The system is sensitive enough toprovide a high signal-to-noise ratio when the extracellular finalconcentration of HPTS is only 1 μM (FIGS. 4A-B).

The equations obtained from the final calibration curves (FIGS. 3A-D,TABLE 4) were used for quantitative analysis of the significant measuredchanges in PBMCs metabolic activity records from all 67 donors (42healthy and 25 cancer patients).

Example 4 Improving Signal-to-Noise Ratio by Dynamic K-MEANS ClusterAnalysis of HPTS Background

Aiming at dynamic clinical evaluation of the MA test results, a reliablemethod was developed that compares any MA test for each donor toprevious tests with respect to the reference rate values of the HPTSsignal (n=730 observations). By this data collection it is possible toimprove the signal to noise ratio of the MA test by filteringexceptional reference results. For that purpose k-means cluster analysis(Nisbet R, I V J E, & Miner G (2009) Handbook of Statistical Analysisand Data Mining Applications) was applied for the accumulatingnormalized rate values of the HPTS probe. In each test at least eightcontrol wells were examined, containing 1 μM HPTS in the workingsolution, without cells and without stimulants. Each value wasnormalized taking into account the accumulated observations using astandard score. FIG. 5A presents the distribution of the standard scoresfor “OPEN” and “CLOSE” rate values of all MA tests. By k-means clusteranalysis 26 clusters were obtained (FIG. 5B), where each observationbelongs to the cluster with the nearest mean. The results reported inFIG. 5D evaluate the stable performance of the probe. Out of all HPTSreference results (n=730) only 5 clusters (4.66%) were discarded. Therest 21 clusters (95.34%) were finally considered to compose the normalreference range. The mean values of “OPEN” and mean values of “CLOSE”states were recalculated for each donor. The k-means cluster analysisallows us to extract the probe background rate signal from the cellulardata and thereby get the actual rate values of the ongoing MA testresults.

Example 5 MA Profiles of Control Samples: (i) at Increasing ReagentsConcentrations in the Absence of Cells; (ii) with Cells but No Reagents

(i) Control experiments in the absence of cells verified that theacidification profiles obtained in the presence of cells indeed measurethe rate of cellular metabolic activity. Thus, no acidification wasobtained under the same protocol applied in the presence of probe,buffer and each reagent at increasing concentration, but with no cells(e.g glucose (FIG. 6A) and PSA (FIG. 8A)), compared to clearacidification changes in samples with cells. The same control resultswere obtained for all reagents. (ii) A basal level of acidification inthe presence of cells was measured in the absence of any reagentsincluding glucose. Generally, this basal level increased with increasingglucose concentrations, verifying clear aspects of cellular metabolicactivity (FIGS. 6B-C). Moreover the MA profiles at the basal statealready revealed the general trend of a diagnostic shift from dominantoxidative phosphorylation preferred by the naive hPBMCs of 69% of thehealthy donors, to dominant aerobic glycolysis (“Warburg effect”)preferred by activated hPBMCs of 60% of various cancer patients. Theseresults emphasize the potential of the MA-test as a diagnostic toolalready at the basal state which is the closer scenario to the in vivostate. However the basal state alone is not enough for clear cutdifferentiation between healthy and cancer patients (Chi-Square,p=0.45). By the present data mining of all the MA-test profiles inresponse to network of reagents reagents (FIGS. 10A-D), it cansignificantly point on 95.24% of healthy donors and 88% of cancerpatients (age≧4° Chi-Square, p<0.0001) and on 90.48% of healthy donorsand 95.24% of cancer patients (22≦age≧81, Chi-Square, p<0.0001).

Example 6 Comparison of MA Profiles at Increasing Glucose Concentration,Obtained for Typical Healthy Donor and Breast Cancer Patient

First, MA profiles of healthy donors (FIGS. 6B, 7A) are strikinglysimilar, despite the significant difference in age (45 (FIG. 6B) vs. 69(FIG. 7A)) and gender. Second, the results in FIG. 6A-C revealsignificant MA profiles differences between two donors that represent atypical healthy donor and a breast in situ cancer patient (at stage 2,and before any treatment). Additionally, in preliminary experimentswhere the MA test was applied on few cases of auto-immune diseases andadditional non-cancer related infectious diseases, different metabolicactivity profiles compared to those obtained for healthy individuals andcancer patients (data not shown) were already revealed. Thesedifferences point on 3 clinical diagnostic indexes of cancer (FIGS.4A-C). Index 1: MA rate “OPEN”>MA rate “CLOSE” in the basal state (cellsin working solution without reagent). Index 2: MA rate “OPEN”>MA rate“CLOSE” for all glucose concentrations. Index 3: MA rate “OPEN” ofcancer>MA rate “OPEN” of healthy and MA rate “CLOSE” of cancer>MA rate“CLOSE” of healthy. Thus, higher values of oxidative phosphorylation areobtained in fresh PBMCs of healthy samples compared to higher values ofaerobic glycolysis in fresh PBMCs of cancer samples. As mentioned in theintroduction (Vander Heiden M G, Cantley L C, & Thompson C B (2009)Understanding the Warburg effect: the metabolic requirements of cellproliferation. Science 324(5930):1029-1033.; Fox C J, Hammerman P S, &Thompson C B (2005) Fuel feeds function: energy metabolism and theT-cell response. Nat Rev Immunol 5(11):844-852; Michalek R D & RathmellJ C (The metabolic life and times of a T-cell. Immunol Rev 236:190-202)these results presumably indicate on in vivo development of the “WarburgEffect” in activated hPBMCs of cancer patients. More detailedobservations on the MA test results obtained for cancer patients (FIG.6C and FIGS. 7B-D) reveal that in the “CLOSE” state the totalacidification rate is less than in “OPEN” state. These results indicateon a volatile basic product which is responsible for partial titrationof the acidity due to lactic acid and CO₂. This titration might bephysiologically required upon the metabolic switch to the non-volatilelactic acid production. This role is related to ammonia (NH₃), which isone of the primary products of protein catabolism and metabolic pathwaysof purines and pyrimidines. In the instant measurement system, asin-vivo, vital cells must maintain the cytoplasm in a constant pH ofabout 7.2-7.4 by simultaneous metabolic secretion of both the acidic andbasic products as described below.

CO₂+H₂O

H₂CO₃

H⁺+HCO₃ ⁻

NH₃+H₂O

NH₄ ⁺+OH⁻

It should be mentioned that in the 3 indexes analysis, differentcombinations were found at different stages of various cancers (e.gcolon, breast, lung, and pancreas) (TABLE 2, above). Therefore, by thesevariations it is believed that the examination of enough data collectionand follow-up of individual donors will provide reliable and moreinformative diagnostic profiles, as demonstrated in the following casestudy (FIGS. 7A-D).

Example 7 Case Study Follow-Up of MA Profiles, at Increasing GlucoseConcentration, Obtained for a 65 Years Old Female with Breast Cancer

With respect to the preliminary MA-test measurements, it should bementioned that one case of thyroid cancer and one of breast cancer werediagnosed by the present test before the physicians. The breast cancercase was followed up along two years as evidence for the sensitiveinformative capability of the MA test compared to typical classificationof staging and treatments. This is the first report of a 2 yearsfollow-up study of a female donor, clinically diagnosed to have breastcancer one year after the MA test had revealed a state of cancer. Forcomparison, MA profiles are presented of a typical healthy 69 years oldmale (FIG. 7A). MA profiles of the cancer patient on TIME ZERO, thepatient clinically diagnosed as having breast cancer one year after theMA test had revealed a state of cancer (I.E.,) (FIG. 7B). The threecancer diagnostic indexes indicate on a shift from oxidativephosphorylation observed in the healthy profiles (FIG. 7A) to aerobicglycolysis observed in the cancer case profiles (FIG. 7B). Namely,positive values for “CLOSE-OPEN” of healthy profiles (FIG. 7A) andnegative values for “CLOSE-OPEN” obtained for the cancer profiles (FIG.7B). The next MA test obtained for this study case was carried out 10.5months later (FIG. 7C), just after routine mammography diagnosis ofbreast idc cancer at stage 2. It should be emphasized that at that timethe patient didn't report any physiological or palpable symptoms.

From this point on, a follow-up MA test was carried out every 3 weeks.One month later, a tumor surgical removal was carried out. Another monthlater a chemotherapy treatment was given every 3 weeks. Each MA test wascarried out after about 20 days of each treatment, and 2 days before thenext treatment. The last MA test presented here (FIG. 7D) was carriedout after the third chemotherapy treatment, namely about 2 months afterbeginning the chemotherapy protocol (time=(+)14.5 months). It should benoticed that according to the three indexes of the MA test, this lasttest already reveals MA profiles (FIG. 7D) characteristic of healthydonor (FIG. 7A). Obviously the MA test verifies a positive trend alongwith the ongoing treatment. By these results, it will be important touse the MA test in a follow-up procedure in order to reveal the trendbehavior of the MA test profile compared to clinical evaluation duringand far behind completion of the chemotherapy treatment. This follow-upprogram is made available by the clinically-oriented MA test which issimple, non-invasive and non-expensive.

Example 8 Comparison of MA Profiles, at Increasing PSA Concentrations,Obtained for Typical Healthy, Breast Cancer Patient and BreastCancer-Recovered Donor

Up till now the MA profiles were examined under increasing concentrationof glucose that were clearly found as a general non-specific clinicaltool for cancer diagnosis. In order to gain more specific cancerclassification, the MA test explores simultaneously various reagents(TABLE 3) such as tissue-specific normal antigens (e.g PSA, MelanA).

Prostate-specific antigen (PSA) is a normal protein produced by cells ofthe prostate gland. The PSA is a cancer-associated tissue-specificnormal antigen. This peptide is recognized by cytotoxic T lymphocytes(CTL). Increasing level in human peripheral blood of this peptide isclinically used as a biochemical diagnostic marker of prostate cancerfor men (Greene K L, et al. (2009) Prostate specific antigen bestpractice statement: 2009 update. J Urol 182(5):2232-2241). But, lowlevels of PSA are released into the female circulation and up to datethe clinical PSA blood test is not used as a diagnostic factor forwomen. However, numerous studies have shown that PSA is not prostatespecific, but is present in some female hormonally regulated tissues,principally the breast and its secretions (Black M H & Diamandis E P(2000) The diagnostic and prognostic utility of prostate-specificantigen for diseases of the breast. Breast Cancer Res Treat 59(1):1-14;Black M H, et al. (2000) Serum total and free prostate-specific antigenfor breast cancer diagnosis in women. Clin Cancer Res 6(2):467-473). Inwomen, PSA is found in female ejaculate at concentration roughly equalto that found in male semen (Wimpis singer F, Stifter K, Grin W, &Stackl W (2007) The female prostate revisited: perineal ultrasound andbiochemical studies of female ejaculate. J Sex Med 4(5):1388-1393;discussion 1393). Three MA profiles are presented in FIGS. 8A-D, atypical healthy woman (FIG. 8B), woman with breast cancer idc in stage 2and before treatment (FIG. 8C), and female recovered from stage 2 breastcancer 18 years ago (FIG. 8D). Tissue-specific stimulants e.g PSA areobserved to induce marked peaks at optimal concentrations in the MA-testprofiles of cancer patients (FIGS. 8A-D). Such profiles are consideredto reflect disease-specific receptor-mediated stimulation and therebyenable to detect specific tumors (e.g breast by PSA, melanoma by melanAstimulation). The results reveal several significant issues. First, theMA profile of healthy women indicate a higher level of oxidativephosphorylation as already reported above for glucose MA profiles (FIGS.6A-C and FIGS. 7A-D). Second, FIG. 8C point on a PBMCs response to PSAby a female with breast idc cancer in stage 2, before any treatment.This profile expresses high metabolic activity rate at the “OPEN” state,already from the basal state. These profiles indicate a high level ofaerobic glycolysis, a phenomena which is similar to activated T-cells asobtained for the glucose MA profiles of cancer patients. Another uniqueprofile of the MA test was revealed for 50 years old female recoveredfrom breast cancer 18 years ago (FIG. 8D). This profile for PSAstimulation behaves more like that of healthy donor (FIG. 8B).Furthermore it reveals higher MA rate in “close” state that indicates adominant oxidiative phosphorylation pathway at increasing PSAconcentrations more than the characteristic profile for healthy donor(FIG. 8B). This profile maybe related to an increased population ofanti-breast cancer memory cells. This relation is consistent with theobservation that following pathogen clearance, surviving effector cellsdifferentiate into long-lived memory cells and revert to an oxidativemetabolic state (Michalek R D & Rathmell J C (The metabolic life andtimes of a T-cell. Immunol Rev 236:190-202).

Example 9 Model Building and Classification Evaluation for the MA TestResults by Data Mining Tools

Multiple MA profiles including a huge number of MA rate values areobtained for each donor. In order to develop a dynamic clinical analysisthat is updated with every new donor, and to extract patterns from thislarge data base, a computer programming using Data Mining tools weredeveloped, which combine methods from statistics and artificialintelligence with database management (Nisbet R, I V J E, & Miner G(2009) Handbook of Statistical Analysis and Data Mining Applications).Two selected cohorts of the MA test results, for both males and females,were analyzed. Since most of the cancer patients studied were older than39, and in order to minimize as much as possible the effect of age, thisanalysis focused on a subgroup of 42 donors with the age above 40 (21healthy donors and 21 cancer patients). In the second cohort the fullset of donors were used from 22-81 years old (n=67, 42 healthy donorsand 25 cancer patients). In order to classify the donors into healthyand cancer individuals, a set from the family of ten different modelsincluding data mining, machine learning and statistical modeling,applying SAS 9.3 and Clementine software (V13.0) were used. Out of theseten models, only four were able to classify with the highest accuracyboth healthy donors and cancer patients. Out of the four models, threewere from the family of decision tree (CHAID, C5, C&R TREE) and one oflog linear model (Logistic Regression). All four are presented in FIGS.9A-D. The decision-tree models are considered to be the bestclassifiers, since these models don't assume any distributions or anyassumptions. The fourth model that didn't perform as good as the othersis the Logistic regression. This kind of model is best suited when inputdata behaves exactly as the assumptions of the model, such asassumptions about distribution and independency. In order to minimize asmuch as possible over-fitting the Logistic Regression model was ranusing the forward selection method, which enabled us to order thevariables by importance and minimize as much as possible the number ofselected variables. Since each model shows different variables/featuresas a function of different antigens, each at different concentrations,it may be possible to combine the present predictions as a function ofmore than one model. When examining the influence of the overallreagents in different concentrations on the accuracy of the models, themaximal number of variables that were chosen was not more than five(FIGS. 9A,9C), which is recommended when sample size is not sufficientto minimize as much as possible the over-fitting. By these initialresults it was possible to order the ten reagents as predictors by theirfrequency of appearance in the various models (FIGS. 9A, 9C). Currentlyit is possible to pinpoint glucose, MBP, Rapamycin, PSA and PMA aspivotal players in the MA test and in relation to the immune system(TABLE 3). The immunological relevance of the other five reagents (TABLE3) is still revealed by the occasional models' choice. In order toevaluate and compare the performance of the models, the presentinventors decided to use the graphical method which is based on thecumulative gain charts produced by Clementine software (V13.0). The gainchart (FIGS. 9B, D) contains two built-in curves, the random curve(black line) and the best fit curve (sky blue line). All models fallbetween these two curves. In this method, greater area between a givencurve and the random curve (black curve) indicate on a better model.Blood's donors modeling and classification results point on models withperformance similar to the best model (FIGS. 9A-D).

Example 10 Evaluating Model Results of the MA Test Using a ValidationSet of 30% of Blood's Donors

A partition process described below enables to combine and comparemodels so as to gain more confidence in the accuracy of the MA testresults, to evaluate the robust level of the models, and to minimizeover-fitting due to small sampling (n=67 blood sample donations). Thedata as described in FIGS. 9A-D was randomly partitioned into two groupsof “Training” and “Testing” using the Clementine software V13.0 9 (FIGS.10A-D). The validation set included 70% of the donors for both cohorts.First cohort includes 42 donors with the age above 40 (FIGS. 10A-B) andthe second cohort includes full set of donors (n=67) from 22-81 yearsold (FIGS. 10C-D). The first 70% of the donors are described on the“Training set”. The “Training set” is used to build the data miningmodel as described in FIGS. 9A-D. The remaining 30% of the donors(“Testing set”) enable to evaluate the classification result on the“Testing set” using the models that were generated in the “Training set”(CHAID, Logistic, C5, C&R tree) (FIGS. 10A. C). The “Training” and“Testing” sets enable to evaluate model results with greater confidenceand eliminate as much as possible the over fitting challenge. In bothcohorts it was found that C5 model gave more robust results where the“Testing” set curve was similar to the “Training” set (FIGS. 10B, D). C5model was the best performer in the “Testing” set while in the“Training” set C&R tree was the best performer. By these results it ispossible to pinpoint glucose, MBP, PMA, PHA, CONA and L-Glutamine aspivotal players in the MA test and in relation to the immune system(TABLE 3). Similarly to the best models in FIGS. 9A-D, these results(FIGS. 10A-D) support the results of FIGS. 9A-D.

A physiological approach to cancer diagnosis is presented here byrelying on the preliminary experimental results of the metabolicactivity profiles obtained for hPBMCs of healthy and cancer patients. Bythis approach the present inventors have designed a simplehigh-throughput, short-time and cost effective optical method of the MAtest using fresh hPBMCs extracted from 10-20 ml blood sample. Strikingdifferences of hPBMCs fingerprinting MA patterns were revealed bypreliminary examination of two clinical groups, 42 healthy individualsand 25 cancer patients. While the hPBMCs MA profiles of the 42 healthydonors indicate a similar preferred oxidative phosphorylation pathway,the hPBMCs of the 25 cancer patients have a wide spectrum of MAprofiles, preferring aerobic glycolysis in correlation with staging andtreatment. One case of thyroid cancer and one of breast cancer werediagnosed by the MA test before the physicians. This breast cancer casewas followed up by the MA test along two years as evidence for thesensitive informative capability of the MA test with respect to typicalclassification of staging and treatments.

The results reported here, encourage further exploration of themetabolic activity of hPBMCs as a mirror image of tumor development(FIGS. 9A-D). Preliminary results clearly reflect common, as well asspecific, features of hPBMCs metabolic pathways under cancer-inducedevasion of the immune system during the pathological development ofdifferent tumors.

A tissue-specific cancer diagnostic index may be provided by the MA-testprofiles at early and late stages of local tumor development by increase(or decrease) of the MA rates relative to those observed for healthydonors. Certain optimal concentrations of tissue-specific antigensshould be sought for the MA-test profiles. Such tissue-specificdiagnostic profiles are expected also at early stages of tumordevelopment, when initial aggressive antitumor immune response isanticipated. By this approach, it can be postulated that in the healthystate, the immune system is responsible for ongoing early detection andeffective eradication of cancer cells in the context of its normalfunction, by scrutinizing all body tissues. The immune system istherefore proposed to detect and eliminate cancer cells by theirexcessive expression of tissue-specific normal antigens. Therefore, inhomeostasis, a balanced level of an effective immune response should bewell controlled, so as to avoid either a decline of effective cytolyticfunction, or such aggressive activity against self normal cells thatmight rather precipitate autoimmune diseases. Thus, unfortunately, inadvanced stages of cancer the immune system is known to be suppressed,or even educated to support cancer development by tumor-infiltratinglymphocytes, which might be a part of the circulating hPBMCs. In thisview, it is further anticipated that at the lethal metastatic phase ofcancer the MA-test profiles of hPBMCs might shift back to reflect anapparent healthy state due to tissue-specific immunity tolerance andanergy, unlike in chronic inflammation. This apparent healthy state maybe exposed by exhaustion of the relevant tissue-specific antigenstimulation.

Example 11 PBMCs Metabolic-Activity Profiles for Increasing GlucoseConcentration Obtained for Typical Healthy, Cancer and Autoimmune LupusDonors

In homeostasis, the immune system activity should be well controlled;hyperactivity is associated with autoimmune diseases while cancerdevelopment is probably related to hypoactivity of the immune system.

Significantly different MA Profiles were obtained for increasing glucoseconcentration obtained for typical healthy, cancer, and autoimmune lupusdonors (FIG. 13).

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

REFERENCES Other References are Listed Throughout the Application

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What is claimed is:
 1. A method of measuring a metabolic activity (MA)of a non-pathogenic immune cell, the method comprising independentlymeasuring in an extracellular environment of the non-pathogenic immunecell, time-dependent acidification profiles due to secretion of: (i)non-volatile soluble metabolic products and volatile soluble metabolicproducts; (ii) non-volatile soluble metabolic products; and (iii)volatile soluble metabolic products; wherein said measuringacidification profile of said (ii) is effected in an air-exposedchamber, and wherein said measuring acidification profile of said (i) iseffected in an air-sealed chamber, and wherein said measuringacidification profile of said (iii) is by subtracting an acidificationprofile of said (ii) from an acidification profile of said (i), andfurther wherein at least one of said time dependent acidificationprofiles is indicative of the metabolic activity of the non-pathogenicimmune cell.
 2. A method of measuring a metabolic activity (MA) of acell, the method comprising independently measuring in an extracellularenvironment of the cell, time-dependent acidification profiles due tosecretion of: (i) non-volatile soluble metabolic products and volatilesoluble metabolic products; (ii) non-volatile soluble metabolicproducts; and (iii) volatile soluble metabolic products; wherein saidmeasuring acidification profile of said (ii) is effected in anair-exposed chamber, and wherein said measuring acidification profile ofsaid (i) is effected in an air-sealed chamber, and wherein saidmeasuring acidification profile of said (iii) is by subtracting anacidification profile of said (ii) from an acidification profile of said(i).
 3. A method of measuring a metabolic activity (MA) of a cell, themethod comprising independently measuring in an extracellularenvironment of the cell, time-dependent acidification profiles due tosecretion of: (i) non-volatile soluble metabolic products and volatilesoluble metabolic products; (ii) non-volatile soluble metabolicproducts; and (iii) volatile soluble metabolic products; wherein saidmeasuring acidification profile of said (ii) is effected in anair-exposed chamber, and wherein said measuring acidification profile ofsaid (i) is effected in an air-sealed chamber, and wherein saidmeasuring acidification profile of said (iii) is by subtracting anacidification profile of said (ii) from an acidification profile of said(i), and further wherein said time-dependent acidification profile dueto secretion of volatile soluble metabolic products and/or saidtime-dependent acidification profile due to secretion of non-volatilesoluble metabolic products are indicative of the metabolic activity ofthe cell.
 4. The method of claim 1, wherein said extracellularenvironment comprises a defined solution having a calibrated buffercapacity.
 5. The method of claim 4, wherein said buffer comprises aphosphate buffered saline.
 6. The method of claim 1, wherein saidnon-pathogenic immune cell is a lymphocyte.
 7. The method of claim 2,wherein said cell comprises a non-pathogenic immune cell.
 8. The methodof claim 7, wherein said non-pathogenic immune cell is a lymphocyte. 9.The method of claim 3, wherein said cell comprises a cancer cell or anon-pathogenic immune cell.
 10. The method of claim 1, wherein saidmeasuring is effected using a non-toxic membrane impermeable probeselected from the group consisting of a pH probe, a CO₂ probe and NH₃probe and a lactate probe.
 11. The method of claim 10, wherein said pHprobe comprises a ratiometric pH probe.
 12. The method of claim 11,wherein said pH probe comprises HPTS.
 13. The method of claim 1, whereinsaid non-volatile metabolites comprise lactate.
 14. The method of claim1, wherein said volatile metabolites comprise NH₃ and CO₂.
 15. Themethod of claim 1, wherein said measuring acidification profiles iseffected at a constant temperature.
 16. The method of claim 15, whereinsaid constant temperature comprises 37° C.
 17. The method of claim 1,further comprising subjecting said cell to a stimulant or inhibitorprior to, or concomitant with measuring said acidification profile. 18.The method of claim 17, wherein said stimulant or inhibitor comprises acell.
 19. The method of claim 17, wherein said stimulant or inhibitorcomprises a cell-free antigen.
 20. The method of claim 18, wherein saidstimulating cell comprises a lymphocytes and said cell comprises anon-syngeneic lymphocyte with respect to said lymphocyte.
 21. The methodof claim 1, wherein said measuring acidification profiles is effected ina commercial fluorescence multi well plate scanner.
 22. The methods ofclaim 1, further comprising separating said cell from said extracellularenvironment.
 23. The method of claim 22, wherein said separating is byficoll separation under centrifugation.